The DOI matches its Crossref record; 71 of the 88 references it cites were found in a registry or an index; 8 of the 27 sources its claims cite were checked for support.
Vision Transformers for Dense Prediction
Open accessAbstract
We introduce dense prediction transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks. We assemble tokens from various stages of the vision transformer into image-like representations at various resolutions and progressively combine them into full-resolution predictions using a convolutional decoder. The transformer backbone processes representations at a constant and relatively high resolution and has a global receptive field at every stage. These properties allow the dense prediction transformer to provide finer-grained and more globally coherent predictions when compared to fully-convolutional networks. Our experiments show that this architecture yields substantial improvements on dense prediction tasks, especially when a large amount of training data is available. For monocular depth estimation, we observe an improvement of up to 28% in relative performance when compared to a state-of-the-art fully-convolutional network. When applied to semantic segmentation, dense prediction transformers set a new state of the art on ADE20K with 49.02% mIoU. We further show that the architecture can be fine-tuned on smaller datasets such as NYUv2, KITTI, and Pascal Context where it also sets the new state of the art. Our models are available at https://github.com/intel-isl/DPT.
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32 Own result · 23 Own interpretation · 11 No basis given · 10 No full text of the sources · 3 Supported · 2 Partly supported · 1 Supported so far · 1 Own method · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“Specifically, we use the recently proposed vision transformer (ViT) [11] as a backbone architecture.”
§ Vision Transformers for Dense Prediction > 1. Introduction
What the sources say
- Supportedcites [11] 11. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. source
Section 3.1 (p. 3) introduces and describes the Vision Transformer (ViT) architecture. The citation identifies the ViT used as the citing authors’ backbone; their choice to use it is not a claim attributed to this source. (LLM, conf=0.93)
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In the paper
“Virtually all existing architectures for dense prediction are based on convolutional networks [6,31,34,42,49,50,53].”
§ Vision Transformers for Dense Prediction > 1. Introduction
What the sources say
- Partly supportedcites [6] 6. Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille. DeepLab: Semantic im- age segmentation with deep convolutional nets, atrous con- volution, and fully connected crfs. TPAMI, 40(4):834-848, 2018. sourceExperimental — accuracy not yet measured
The Introduction (p. 1) says convolutional models such as PixelCNN had received much more attention for pixel-by-pixel image generation. It does not establish that virtually all architectures across the broader category of dense prediction are convolutional; it also discusses recurrent models and proposes a self-attention alternative (Introduction, p. 2). (LLM, conf=0.78)
- Not checked — no full text of the sourcecites [31] 31. Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U- Net: Convolutional networks for biomedical image segmen- tation. In MICCAI, 2015. source
- Supportedcites [34] 34. Evan Shelhamer, Jonathan Long, and Trevor Darrell. Fully convolutional networks for semantic segmentation. CVPR, 2015. source
The paper is an example of a convolutional-network architecture for dense prediction: its Abstract (p. 1) describes predicting a depth map, and §3.1.2 (p. 3) identifies its convolutional layers and target-depth output. As one of several citations, it supports the sentence by exemplifying such an architecture, rather than establishing the prevalence claim on its own. (LLM, conf=0.88)
- Could not verifycites [42] 42. Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, and Bin Xiao. Deep high-resolution representation learning for visual recogni- tion. TPAMI, 2020. sourceExperimental — accuracy not yet measured
Section 2.1 (p. 8) describes transformer language-model architectures, not architectures for dense prediction. Searches for dense prediction, convolutional networks, and pixel-wise segmentation found no passage addressing whether dense-prediction architectures are predominantly convolutional. (LLM, conf=0.78)
- Not matchedcites [49] 49. Fisher Yu and Vladlen Koltun. Multi-scale context aggrega- tion by dilated convolutions. In ICLR, 2016.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [50] 50. Yuhui Yuan, Xilin Chen, and Jingdong Wang. Object- contextual representations for semantic segmentation. In ECCV, 2020. source
- Not checked — no full text of the sourcecites [53] 53. Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In CVPR, 2017. source
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In the paper
“We notice that the biggest gains in performance for zero-shot transfer were achieved for datasets that feature dense, high-resolution evaluations [15,55,59].”
§ Vision Transformers for Dense Prediction > Additional information
What the sources say
- Not checked — no full text of the sourcecites [15] 15. Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark suite. In CVPR, 2012. source
- Supportedcites [55] 55. D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black. A naturalistic open source movie for optical flow evaluation. In ECCV, 2012. source
The citation identifies MPI-Sintel as one of the datasets in the citing authors’ comparison; the performance gains are the citing authors’ observation, not a result attributed to this paper. Design Decisions and Comparison to Middlebury (p. 5) describes Sintel’s higher-resolution frames, and The Sintel Data Set (p. 9) describes its flow fields and pixel-level error evaluation. (LLM, conf=0.86)
- Not checked — no full text of the sourcecites [59] 59. Thomas Schöps, Johannes L. Schönberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and An- dreas Geiger. A multi-view stereo benchmark with high- resolution images and multi-camera videos. In CVPR, 2017. source
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In the paper
“Note that ResNext101-WSL was pretrained on a billion-scale corpus of weakly supervised data [25] in addition to ImageNet pretraining.”
§ Vision Transformers for Dense Prediction > Skip connections.
What the sources say
- Partly supportedcites [25] 25. Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten. Exploring the limits of weakly supervised pretraining. In ECCV, 2018. sourceExperimental — accuracy not yet measured
Section 2.3 (p. 4) identifies the models as ResNeXt-101, and Section A.5 (p. 23) says they were pretrained on billions of Instagram images using hashtags as labels. But it describes subsequent training on ImageNet as fine-tuning, not additional ImageNet pretraining; Section 3.2 (p. 12) also distinguishes Instagram-pretrained models from ImageNet-pretrained baselines. (LLM, conf=0.88)
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In the paper
“While ignoring the token yields good performance, projection provides slightly better performance on average.”
§ Vision Transformers for Dense Prediction > Skip connections.
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In the paper
“Adding the token, on the other hand, yields worse performance than simply ignoring it.”
§ Vision Transformers for Dense Prediction > Skip connections.
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In the paper
“Both DPT variants significantly outperform the state of the art.”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“Note that both architectures have similar latency to MiDaS (Table 9).”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“ViT-Large outperforms all other backbones but is also almost three times larger than ViT-Base and ViT-Hybrid.”
§ Vision Transformers for Dense Prediction > Skip connections.
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In the paper
“We again see that DPT can provide strong performance even on smaller datasets.”
§ Vision Transformers for Dense Prediction > ADE20K.
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- Probably not a substantial claim. 11
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Every source the paper cites for this claim was checked and supports it.
In the paper
“In particular, it has recently been demonstrated that a direct application of token-based transformer architectures that have been successful in NLP can yield competitive performance on image classification [11].”
§ Vision Transformers for Dense Prediction > 2. Related Work
What the sources say
- Supportedcites [11] 11. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. source
The Introduction (p. 1) says image patches are treated like NLP tokens and fed to a standard Transformer. The Conclusion (p. 9) reports that this approach matches or exceeds the state of the art on many image-classification datasets when coupled with pre-training on large datasets. (LLM, conf=0.93)
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In the paper
“A key insight of this work was that, like transformer models in NLP, vision transformers need to be paired with a sufficient amount of training data to realize their potential.”
§ Vision Transformers for Dense Prediction > 2. Related Work
What the sources say
- Supportedcites [11] 11. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. source
The Introduction (p. 1) says ViT performs modestly on mid-sized datasets and does not generalize well with insufficient data, but attains excellent results when pre-trained at sufficient scale. Section 4.3 (p. 6) reports that larger ViT models show their full benefit only with the largest pre-training dataset tested. The Conclusion (p. 9) also describes the approach as working well when coupled with pre-training on large datasets. (LLM, conf=0.94)
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In the paper
“Similarly, the ViT-Hybrid architecture extracts features at 1 16 the input resolution, which is twice as high as the lowest-resolution features that are commonly used with convolutional backbones.”
§ Vision Transformers for Dense Prediction > 3. Architecture
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In the paper
“It contains about 1.4 million images and is, to the best of our knowledge, the largest training set for monocular depth estimation that has ever been compiled.”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“When applied to semantic segmentation, dense vision transformers set a new state of the art on ADE20K with 49.02% mIoU.”
§ Vision Transformers for Dense Prediction > Abstract
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In the paper
“We observe that the DPT tends to produce cleaner and finer-grained delineations of object boundaries and that the predictions are also in some cases less cluttered.”
§ Vision Transformers for Dense Prediction > ADE20K.
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In the paper
“DPT can better reconstruct fine details while also improving global coherence in areas that are challenging for the convolutional architecture (for example, large homogeneous regions or relative depth arrangement across the image).”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“The average relative improvement over the best published architecture, MiDaS, is more than 23% for DPT-Hybrid and 28% for DPT-Large.”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“DPT-Hybrid achieves this with a comparable network capacity (Table 9), while DPT-Large is about 3 times larger than MiDaS.”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“We observe the tendency that attention is spatially more localized close to the reference token in shallow layers (leftmost columns), whereas deeper layers (rightmost columns) frequently attend across the whole image.”
§ Vision Transformers for Dense Prediction > Additional information
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In the paper
“ViT-Base has comparable performance to ResNext101-WSL, while ViT-Hybrid and ViT-Large improve performance even though they have been pretrained on significantly less data.”
§ Vision Transformers for Dense Prediction > Skip connections.
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In the paper
“While we observe a general trend of an improvement in per-class IoU in comparison to the baseline [51], we do not observe a strong pattern across classes.”
§ Vision Transformers for Dense Prediction > Additional information
What the sources say
- Not checked — no full text of the sourcecites [51] 51. Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Muller, R. Manmatha, Mu Li, and Alexander Smola. ResNeSt: Split- attention networks. arXiv preprint arXiv:2004.08955, 2020.
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In the paper
“While we observe a general trend of an improvement in per-class IoU in comparison to the baseline [51], we do not observe a strong pattern across classes.”
§ Vision Transformers for Dense Prediction > Additional information
What the sources say
- Not checked — no full text of the sourcecites [51] 51. Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Muller, R. Manmatha, Mu Li, and Alexander Smola. ResNeSt: Split- attention networks. arXiv preprint arXiv:2004.08955, 2020.
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In the paper
“We observe more details and also better global depth arrangement in DPT predictions when compared to the fullyconvolutional baseline.”
§ Vision Transformers for Dense Prediction > Additional information
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In the paper
“The transformer backbone processes representations at a constant and relatively high resolution and has a global receptive field at every stage.”
§ Vision Transformers for Dense Prediction > Abstract
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In the paper
“As long as the image size is divisible by p, the embedding procedure can be applied and will produce a varying number of image tokens N p .”
§ Vision Transformers for Dense Prediction > 3. Architecture
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In the paper
“Unlike fully-convolutional networks, the vision transformer backbone foregoes explicit downsampling operations after an initial image embedding has been computed and maintains a representation with constant dimensionality throughout all processing stages.”
§ Vision Transformers for Dense Prediction > 1. Introduction
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In the paper
“Since transformers are known to realize their full potential only when an abundance of training data is available, monocular depth estimation is an ideal task to test the capabilities of DPT.”
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
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In the paper
“This is in stark contrast to convolutional networks, which progressively increase their receptive field as features pass through consecutive convolution and downsampling layers.”
§ Vision Transformers for Dense Prediction > 3. Architecture
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In the paper
“DPT-Large performs slightly worse, likely because of the significantly smaller dataset compared to our previous experiments.”
§ Vision Transformers for Dense Prediction > ADE20K.
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In the paper
“DeIT trains the ViT architecture with a more data-efficient pretraining procedure.”
§ Vision Transformers for Dense Prediction > Skip connections.
What the sources say
- Not checked — no full text of the sourcecites [38] 38. Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers & distillation through at- tention. arXiv preprint arXiv:2012.12877, 2020. source
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In the paper
“Note that the DeIT-Base architecture is identical to ViT-Base, while DeIT-Base-Dist introduces an additional distillation token, which we ignore in the Reassemble operation.”
§ Vision Transformers for Dense Prediction > Skip connections.
What the sources say
- Not checked — no full text of the sourcecites [38] 38. Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers & distillation through at- tention. arXiv preprint arXiv:2012.12877, 2020. source
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In the paper
“Similar to prior work on transformers, DPT unfolds its full potential when trained on large-scale datasets.”
§ Vision Transformers for Dense Prediction > 5. Conclusion
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In the paper
“Note that results for DPT and Mi-DaS are computed at the same input resolution (384 pixels).”
§ Vision Transformers for Dense Prediction > Additional information
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
The choice of backbone architecture has a large influence on the capabilities of the overall model, because information lost in the encoder cannot be recovered in the decoder.
“However, it is widely recognized that the choice of backbone architecture has a large influence on the capabilities of the overall model, as any information that is lost in the encoder is impossible to recover in the decoder.”
The paper relies on this without support.
Feature resolution and granularity are critical for dense prediction, where the architecture should ideally resolve features at or close to the input resolution.
“While feature resolution and granularity may not matter for some tasks, such as image classification, they are critical for dense prediction, where the architecture should ideally be able to resolve features at or close to the resolution of the input image.”
The paper relies on this without support.
Convolutions are linear operators with a limited receptive field.
“Convolutions, by definition, are linear operators that have a limited receptive field.”
The paper relies on this without support.
Downsampling intermediate representations is necessary to keep memory consumption at levels feasible with existing computer architectures.
“Downsampling the intermediate representations is necessary to keep memory consumption at levels that are feasible with existing computer architectures.”
The paper relies on this without support.
The transformer backbone maintains a constant feature resolution.
“Since the transformer backbone maintains a constant feature resolution, it is not clear at which points in the backbone features should be tapped.”
The paper relies on this without support.
Performance of fully-convolutional architectures suffers heavily when performing inference at an input resolution significantly different from the training resolution.
“Performance thus suffers heavily when performing inference at an input resolution that is significantly different from the training resolution.”
The paper relies on this without support.
Transformer encoders have a global receptive field in every layer.
“Transformer encoders, on the other hand, have a global receptive field in every layer.”
The paper relies on this without support.
The reduced meta-dataset datasets provide high-quality ground truth.
“We choose these datasets since they provide high-quality ground truth.”
The paper relies on this without support.
Convolutional architectures offer natural points of interest for passing features from the encoder to the decoder, namely before or after downsampling of the representation.
“Convolutional architectures offer natural points of interest for passing features from the encoder to the decoder, namely before or after downsampling of the representation.”
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In the citing paper
“Vision transformers, which are a deep learning approach to encoding low-dimensional input into a high dimensional feature space, have established new frontiers in depth estimation compared to convolutional neural networks (Ranftl et al., 2021).”
From Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar (2024) · cites this paper as Ranftl et al., 2021
In the citing paper
“However, the Transformer framework does not involve dimensionality reduction10,12, thus keeping all attention layer traceable to the original input features13, thus making the models interpretable.”
From Transformer for one stop interpretable cell type annotation (2023) · cites this paper as [10]
In the citing paper
“For example, the change of dimensions and non-linear aggregation of features throughout the autoencoder’s deep processing stages leads to untraceable and uninterpretable latent space and loss of information and feature resolution9,10.”
From Transformer for one stop interpretable cell type annotation (2023) · cites this paper as [10]
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In the paper
§ Vision Transformers for Dense Prediction > 4.1. Monocular Depth Estimation
“Our architecture matches or improves state-of-the-art performance on both datasets in all metrics.”
§ Vision Transformers for Dense Prediction > 5. Conclusion
“| BTS [20] | 0.956 | 0.993 | 0.998 | 0.059 2.756 | 0.096 | | DPT-Hybrid 0.959 | 0.995 | 0.999 | 0.062 2.573 | 0.092 |”
Citations
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InfluentialBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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BackgroundLanguage Models are Few-Shot Learners
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Sorted by citations
Items 1,701–1,800 of 2,248
SpatialBoost: Enhancing Visual Representation Through Language-Guided Reasoning
Jeon, B., Kim, D., Jang, H., Kim, I., & Shin, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37393-9_23 · Cited by 2
Towards Geometry-Grounded Dense Semantic Matching with VGGT Priors
Yang, S., Wei, T., Lan, Y., Xiao, Z., Rao, A., & Pan, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37098-3_32
StereoGS: Sparse-View 3D Gaussian Splatting via Stereo Priors
Yuan, W., Ge, Y., & Cai, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37393-9_6
Effective Text-Directed Scanpath Prediction via Comprehensive Multi-modal Information Fusion
Liu, Y., & Quan, R. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5702-8_14
HiFused-Depth: High-Frequency Fused Depth Estimation With Knowledge Guidance
Ji, G., Hu, X., Chen, T., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3608944
“However, since convolutional neural networks are insensitive to high-frequency information [49], there is still a significant bottleneck in the prediction performance of existing methods in low-texture regions.”
Real-Time Obstacle Avoidance Using Monocular Depth Estimation and Optical Flow Techniques
Shaikh, M. A., Syed, M. A., Yadav, C., et al. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-96-6250-0_40
Label-guided class similarity distillation for continual semantic segmentation
Wang, B., Li, Y., Yang, S., Huang, T., Zhao, Z., & Zhang, S. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-026-03128-y
“More recently, Transformer architectures have gained widespread adoption, achieving breakthrough progress in semantic segmentation tasks due to their powerful feature modeling capabilities [ 30–35 ].”
OrchardDepth++: Binned KL-Flood Regularization for Monocular Depth Estimation of Orchard Scene
Zheng, Z., Williams, H. A., Gee, T., & MacDonald, B. A. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247466
Learning to Build by Building Your Own Instructions
Walsman, A., Zhang, M., Fishman, A., Farhadi, A., & Fox, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73024-5_16 · Cited by 1
UH-Net: U-Net with hybrid attention for full waveform inversion
Zhang, Z., Qin, Z., Liu, L., et al. (2026) · Acta Geophysica · doi:10.1007/s11600-026-01870-z
MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources
Yang, J., Di, D., Zhang, X., Fan, L., Cui, J., Li, H., & Ma, B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37016-7_16 · Cited by 6
Yang, J., Li, L., Li, W., & Zheng, S. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114097 · Cited by 1
Lightweight Transformer Model for Agricultural Land Use and Land Cover Classification
Çelik, K. (2025) · Tarım Bilimleri Dergisi · doi:10.15832/ankutbd.1624812
“In contrast to recurrent network-based models, the Transformer's encoder-decoder architecture handles sequential data in a parallel manner (Ranftl et al. 2021).”
Toward Accurate Camera-based 3D Object Detection via Cascade Depth Estimation and Calibration
Wang, C., Qin, Y., Kang, Z., Ma, N., & Zhang, R. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610281
“The former [13], [14], [15], [16], [17] regard the depth estimation task as a regression or classification problem of depth distribution.”
BackgroundXia, C., Zhang, M., Gao, X., Ge, B., Li, K., Fang, X., Zhang, Y., & Liang, X. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06205-7
“Firstly, we compare our encoder with different mainstream frameworks, including ResNet-50 [63], ResNet-101 [63], ViT-Base [13] and MPViT [31].”
InfluentialSemantic-Enhanced Monocular Depth Estimation via Fusion and Distillation of Foundation Models
Ma, S., Choi, W., Park, J., Kim, J., & Im, S. (2026) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic69189.2026.11385908
“Table I presents a comparison with six state-of-the-art relative depth estimation methods [4]– [7], [20], [28] across four benchmarks [24]–[27].”
Haiyang, 胡. H., Chaoping, 陈. C., Tianmu, 高. G., Baoen, 韩. H., Yunfan, 杨. Y., Yi, 刘. L., & Xiaojun, 武. W. (2025) · Infrared and Laser Engineering · doi:10.3788/irla20250157
MonSter++: Unified Stereo Matching, Multi-View Stereo, and Real-Time Stereo With Monodepth Priors
Cheng, J., Liao, W., Cai, Z., Liu, L., Xu, G., Wang, X., Wang, Y., Yuan, Z., Deng, Y., Zang, J., et al. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3708185 · Cited by 25
“Our best empirical configuration uses pretrained DepthAnythingV2 [38] as the monocular depth branch, which uses DINOv2 [58] as the encoder and DPT [77] as the decoder.”
Xiong, Q., Li, X., Wang, K., et al. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464648
Yang, Y., Wang, T., Cai, Y., et al. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-0291-1_27
Hf-Depth: Monocular Depth Estimation Using High-Frequency Features to Enhance Structural Modeling
Zhao, L., Gao, A., Chen, Q., et al. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3726906
“Compared with representative methods such as AdaBins [16], DPT-Hybrid [6], and IEBins [34], the proposed method demonstrates competitive depth estimation performance, which verifies the e ff ectiveness of the proposed framework for monocular depth estimation.”
CasaMaestro: Multi-view Panoramas for House-Scale 3D Reconstruction
Ji, Y., Yang, X., & Zhang, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37252-9_12
Triplet Vision Transformer for Word Image Retrieval in Historical Arabic DocumentsPreprint
Fathallah, A., El‐Yacoubi, M. A., & Amara, N. E. B. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4577874
PGCR: Pose-Geometry Coupled Reasoning for Image-to-Point Cloud Registration
Li, X., Yang, W., Chen, Y., Cheng, Z., Zhang, S., Zhou, X., & Zhang, T. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37032-7_36
Omni-Recon: Harnessing Image-Based Rendering for General-Purpose Neural Radiance Fields
Fu, Y., Qu, H., Ye, Z., Li, C., Zhao, K., & Lin, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72640-8_9
“For these tasks, the 2D monocular priors are provided by [51], [5], and [56], respectively.”
MethodVCINet: Visual Cue-Inspired Feature Learning Network for SAR Building Segmentation
Wang, M., Yan, Z., Diao, W., et al. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3579753
Feature enhancement module for HD fusion map
Li, J., & Su, Y. (2025) · International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID) · doi:10.1117/12.3061190
“Although the above works have achieved certain successes, they all rely on the powerful representational capabilities of attention mechanisms[9], which come with high computational costs.”
Chang, Q., Xu, J., Cui, Y., Zhai, Y., Coscia, P., Genovese, A., Piuri, V., & Scotti, F. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3623503
“The architecture employs DPT [26] as the backbone network.”
Analysis of Depth Sensing and Lane Detection Algorithms for Advanced Driver Assistance Systems
Sarkar, S., Khan, F. H., Das, S., et al. (2023) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-031-25194-8_4
Sanatani, R. P., & Nagakura, T. (2023) · Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia · doi:10.52842/conf.caadria.2023.1.555
DepthSeg: Depth Prompting in Remote Sensing Semantic Segmentation
Zhou, N., Chen, S., Zhou, M., Sui, H., Hu, L., Li, H., Li, H., & Zhou, Q. (2025) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss55030.2025.11242444
“Specifically, a dense prediction transformer[14] is introduced to extract depth information, and a depth prompter is introduced to encode depth features, effectively guiding the semantic segmentation.”
VARE: Geometry-Anchored Bearing and Range Stabilization for USV Recovery
Chen, C., Sun, Z., Zhang, J., Zhang, P., Dong, J., Qian, R., & Wang, D. (2026) · Sensors · doi:10.3390/s26175347
“Transformer-based models have improved cross-domain depth prediction [15,27].”
Dual-Guided Semi-Supervised Semantic Segmentation for Citrus Quality Evaluation
Xu, X., Guo, R., Guo, K., et al. (2026) · Foods · doi:10.3390/foods15112029
“Both the EMA teacher and student models employ the Dense Prediction Transformer (DPT) architecture [22].”
UDepthDiff: Multi-Modal Physical Diffusion-Based Underwater Monocular Depth Estimation
Zhu, H., & Xing, Z. (2026) · IEEE Access · doi:10.1109/access.2026.3651731
“Notable contributions include ZoeDepth for zero-shot transfer [18], DSCNet for indoor scenes [19], and dense vision Transformers for dense prediction [20]; however, even these hybrid architectures may compromise fine structural details.”
Yang, R., Liu, Y., Zhong, R., Liu, S., Wei, Z., Xu, M., & Yan, P. (2026) · Big Earth Data · doi:10.1080/20964471.2026.2634534
MonoDeMB: Comprehensive Monocular DepthMap Benchmark
Chopuryan, V., Kuznetsov, M. D., Latonov, V., Mashurov, V., & Semenova, N. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737394
“The Depth Anything model presented in [82] was tested in and out of the training domain and outperformed the models with which it was compared, in particular AdaBins, ZoeDepth and DPT [56].”
Evaluating Foundational Depth Models for Zero-shot 3D Box Lifting from 2D detections
loulou, A., Aydemir, E., & Ünel, M. (2026) · International Conference on Consumer Electronics · doi:10.1109/icce67443.2026.11449883
“Architectures such as DPT [9], LeReS [10], ZeoDepth [11] and Depth-pro [7] have shown strong zero-shot performance by leveraging massive pretraining on mixed data sources.”
Dense Depth-Supervised Simultaneous Localization and Mapping for Robust Bronchoscopic Navigation
Huang, X., Fan, W., Fang, H., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-0033-8_42
BP-NeRF: End-to-End Neural Radiance Fields for Sparse Images Without Camera Pose in Complex Scenes
Qiu, Y., Wu, G., & Sun, Y. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3657188
“10, the first column shows the initial depth map obtained by the monocular depth prediction algorithm [46], the second column shows the depth map output by DDP-NeRF, and the third column shows the depth map output by our method.”
Robust Monocular Depth Estimation Under Crop-Resize-Induced Intrinsics Mismatch
Kim, H., & Lee, D. (2026) · Electronics · doi:10.3390/electronics15102180
Kiran, D., & Roy, U. M. (2026) · Journal of Innovative Image Processing · doi:10.36548/jiip.2026.4.005
Liu, Z., Jing, D., & Ji, C. (2025) · Symmetry · doi:10.3390/sym17091428
Editing Reality: Designing In-Situ Co-Creation with Generative AI in Mixed Reality
Weng, S. C., Ma, S.-Y., Reinig, S., Kadam, P., Zhao, A. Y., Banic, A. U., Suzuki, R., & Yi–Luen, E. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3813087 · Cited by 2
“A depth map (e.g., MiDaS [42]) is used to build a coarse patch mesh, which is composited using a stencil/occlusion shader aligned to the current pose.”
Multi-Modal Person Re-Identification Using Depth Estimation and Edge DetectionPreprint
Rayani, M. S., Keshavarz, A., Ghimatgar, H., et al. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4625411
RDG-GS: Relative Depth Guidance with Gaussian Splatting for Real-time Sparse-View 3D Rendering
Zhan, C., Zhang, Y., Lin, Y., et al. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-025-02594-1
Vanitha, K. M., Jayanth, V., Anurag, T., et al. (2026) · doi:10.1109/ecmi68341.2026.11603094
“The navigation module provided obstacle - aware directional guidance by integrating MiDaS depth estimation [16], [17] and YOLOv8 object detection on the Colab server.”
Diffusion-Based Synthetic Petri Dish Images Generation for CFU Detection Model Training
Quevit, V., Dillenseger, J., Laferté, J.-M., et al. (2026) · IRBM · doi:10.1016/j.irbm.2026.100964
Spatial Semantic Enhancement Guided Learning and Consistency Constraints for Object Detection
Cao, W., Liu, X., Kuwan, Y., et al. (2026) · 2026 7th International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl70130.2026.11637619
“Compared with RGB images alone, depth maps contain explicit distance and geometric boundary information and can provide natural advantages in object separation, occlusion reasoning, and scale estimation [13] .”
Geometric Probing for Isotropic Optimization Manifold in Sparse-View 3D Gaussian Splatting
Zhao, Y., Deng, X., Xu, H., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36839-3_5
Reconstruction-aware token merging for efficient human-scene 3D reconstruction
Li, Y., Hou, Y., Qiao, X., Zhou, Q., Zhang, H., & Yang, Q. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114777
VisLanding: Monocular 3D Perception for UAV Safe Landing via Depth-Normal Synergy
Tan, Z., He, B., Ji, Y., et al. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247278
“Subsequently, the encoder extracts multi-scale features from the image, and the decoder predicts the initial canonical depth D 0 and unnormalized normal N 0 .”
Vision-Based Autonomous UAV Low-Altitude Road Following and Obstacle Avoidance
Lee, G.-Y., & Liu, J. S. (2024) · Wireless and Optical Communications Conference · doi:10.1109/wocc61718.2024.10786036
“Additionally, Monodepth2[18] and DPT[19] employ deep learning models for monocular image depth estimation, with DPT also leveraging the ViT mechanism.”
Zhang, X., Cheng, H., Xu, T., Li, X., Cao, D., Dong, X., Li, Y.-A., Liu, X., & Wu, Y. (2026) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2026.104934
Detail-Preserving Self-Supervised Monocular Depth with Self-Supervised Structural Sharpening
Bello, J. L. G., Moon, J.-H., & Kim, M. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00031
“More recent works have also explored the joint learning of SVDE, and depth completion as in [14], while others have explored incorporating vision transformers for dense prediction [35].”
BackgroundRobust Monocular Depth Estimation Against Adversarial Camouflage in Autonomous Vehicles
Khanna, S., Hafeez, M. A., Asad, M., et al. (2025) · IEEE International Conference on Vehicular Electronics and Safety (ICVES) · doi:10.1109/icves65691.2025.11376412
“Initial Testing The effectiveness of the optimized adversarial patches was evaluated by testing them on six state-of-the-art MDE models: Monodepth2 [28], DenseDepth [35], DepthAnythingV2 [34], ZoeDepth [37], DPT [38], AdaBins [31] and MiDaS-L [30].”
Is Physical Depth Sensor Necessary for Visual Odometry? Geometry-Aware Fine-Tuning
Yang, S., Wang, Y., & Zhang, J. (2026) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac71005.2026.11615440
“The DPT architecture [9] established that transformer-based decoders can progressively fuse multi-scale tokens from a Vision Transformer (ViT) encoder to achieve high-resolution dense predictions.”
Robust RGB–Thermal Pedestrian Detection with Monocular Depth Prior
Deepa, G., Kolluri, J., & Siripuri, K. (2026) · doi:10.1109/icicit69063.2026.11634032
“Monocular depth estimation models like MiDaS [14], [17] and DPT can produce relative depth from a single RGB image [8].”
Li, Z., Zhu, K., & Jin, G. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02062-3
AerialMetric: Benchmarking and Adapting UAV Monocular Metric Depth Estimation in the Real World
Song, Z., Chen, G., Zhang, Y., Zou, Y., Fu, C., Yuan, Z., Huang, C., Cui, S. R., & Cao, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36984-0_20
Geometric Edge Modelling in Self‐Supervised Learning for Enhanced Indoor Depth Estimation
Joswig, N., & Ruotsalainen, L. (2025) · IET Computer Vision · doi:10.1049/cvi2.70026
LumiDepth: Stable Monocular Depth in Multi-illumination Scenes
Cheng, A., Yang, Z., Li, T., Zhu, H., & Mao, K. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37314-4_9
Adversarial 3D Objects Against Monocular Depth Estimators
Fehér, T. M., & Szemenyei, M. (2024) · International Conference on Machine Learning Technologies (ICMLT) · doi:10.1145/3674029.3674052
WPIS: From In-the-Wild Web Images to Physics-Aware 3D Scene Graphs for Physical Reasoning
Ma, K., Fu, C., Wang, J., Wang, Y., Li, W., Wang, X., Wang, M., & Xia, T. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792591
Real‐Time GPS Coordinate Estimation of an Object Using a Monocular Camera
Han, T.-T., Duc, M. T., Tien, T. T., et al. (2026) · Journal of Electrical and Computer Engineering · doi:10.1155/jece/5386020
Distortion-Aware Bi-Projection Fusion for 360∘ Monocular Depth Estimation via Coordinate Attention
Geng, L., Li, M., Qin, Y., et al. (2026) · Electronics · doi:10.3390/electronics15143066
Yin, Z., Yan, R., Chang, S., Xiao, Y., & Yu, X. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5462738
Efficient Monocular Depth-Based Physical Distance Measurement for Low-Depth Scales
Yang, J., Zinke, M., Kang, B., Cho, H. U., Choi, H.-Y., Lee, S.-G., & Kang, S. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3598456
“Recently, dense prediction transformer (DPT) [16], which uses the transformer structure, has further improved depth estimation performance.”
InfluentialTarun, A., Chaudhury, K. S., Changmai, A., et al. (2025) · International Conference on Circuits, Power and Intelligent Systems (CCPIS) · doi:10.1109/ccpis65231.2025.11234161
“The overall process utilises the Dense Vision Transformer(DPT) [11] for first phase, which has an encoder-decoder structure that is effective in dense prediction cases.”
Learning multiplane images from single views with self-supervision
Carvalho, G. S. P., Luvizon, D. C., Neto, A. J., Pacheco, A. G. C., & Penatti, O. A. B. (2021) · British Machine Vision Conference (BMVC) · doi:10.5244/c.35.23 · Cited by 2
“One could also argue that the DPT model [21] was trained with multiple datasets, including stereo data.”
BackgroundMethodInfluentialSS-NeRF: Physically Based Sparse Spectral Rendering With Neural Radiance Field
Li, R., Liu, J., Liu, G., Zhang, S., Zeng, B., & Liu, S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3611376
“DPT Hybrid [28] model is selected as the monocular depth estimator for the depth-aware continuity.”
InfluentialWu, C.-Y., Zhong, Y., Wang, J., & Neumann, U. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801638
“• The first method to apply meta-learning on pure single image depth prediction, which helps achieve higher generalization in depth estimation without multiple training datasets, side information, or other pretrained networks.”
PFE-Net: Proxy-Guided Frequency Enhancement for Camouflaged Object Detection
Dong, T., Tu, Y., Xu, C., et al. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464498
Hierarchical Consistency Learning for Test-Time Adaptation in Camouflage Perception
Zha, M., Li, T., Wang, G., et al. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3705217
“We evaluate state-of-the-art methods on four underwater benchmarks under the same three degradation settings, and further integrate HCL to explore its performance gains.”
Video-Based Self-supervised Human Depth Estimation
Li, Q., & Zhang, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-50069-5_16
Yan, L., Li, Q., Li, Z., et al. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-026-03036-1
Low-Resource Adaptation and Structural Reconstruction for Satellite Component Segmentation
Hong, R., Zhai, C., Chen, L., Hu, H., Pan, H. C., & Wang, Q. (2026) · Aerospace · doi:10.3390/aerospace13090793
“Rather than relying solely on the final-layer representation, the proposed aggregation exploits intermediate structural features that preserve richer local geometry while maintaining high-level semantic consistency [38].”
Deng, W., Li, H., & Shen, H. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3626844
Kiroshi: An Agentic Perception System for High-Accuracy Image Parsing
Zhou, H., Liu, J., Zhang, H., Lu, X., Ma, J., & Zhu, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37242-0_11
He, Z., & Octavia, A. (2026) · International Conference on Digital Society and Artificial Intelligence (DSAI) · doi:10.1145/3833541.3833679
“1-8B [32]: 25.9 → 52.9%), with Recover Rate improving by over 42.8% for Qwen3-4B while [33], hand segmentation [34], and 3D ray-casting improves Recover Rate by 3.2–6.6%.”
InfluentialApurbo, M. A. A. M., Shamim, M. M. I., & Rahman, M. S. (2026) · doi:10.1109/qpain69676.2026.11545615
“Our MiDaS model is built based on a DPT-based backbone and trained on large-scale mixed datasets and used in a zero-shot fashion [14].”
Analysis of Modern Landscape Architecture Evolution Using Image-Based Computational Methods
Zhang, J., & Chi, G. (2025) · Mathematics · doi:10.3390/math13172806
Implicit and Explicit Language Guidance for Diffusion-Based Visual Perception
Wang, H., Cao, J., Xie, J., Yang, A., & Pang, Y. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3521825 · Cited by 5
Mendes, R. D. Q. (2021) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.18.2021.tde-02092021-110320
Monocular Depth Estimation on an Edge Device Using MiDaS DPT Hybrid
Roazario, A. P. R., Suresh Babu, B., Yashwanth Daran, D., et al. (2025) · International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems (iTech SECOM) · doi:10.1109/itechsecom64750.2025.11307624
Widodo, A. S., Natalia, K. E., Yoswandi, J. O., et al. (2026) · doi:10.1109/isiber68248.2026.11470552
“These techniques have the advantage of being pre-trained extensively on a number of datasets to ensure robust generalization capabilities for complex cultural heritage items that contain complex geometric characteristics [21].”
Song, Z., Fan, X., Shen, Y., Tjahjadi, T., Li, Y., Ye, Q., & Fu, L. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3692661
“For example, Ranftl et al. [45] introduced the ViT to e ff ectively model long-range relationships, while Agarwal and Arora [48] implemented PixelFormer, a transformer-based encoder–decoder architecture with skip connections linking the encoder and decoder.”
InfluentialRizaluddin, B., Sari, Y. A., & Adinugroho, S. (2025) · doi:10.1109/icdsg67714.2025.11381375
“For comparative analysis, baseline depth models such as MiDas-DPT [2, 7] and ZoeDepth [1] were also tested under identical preprocessing and scaling.”
MegaFlow: Zero-Shot Large Displacement Optical Flow
Zhang, D., Wang, F., Pollefeys, M., & Xu, H. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37321-2_19 · Cited by 6
DepthShield: Robust Depth Estimation on Transparent or Mirror Surfaces
Zhu, W., Zhang, Y., Lan, L., Che, Y., & Xiao, C. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463855
Review of Hybrid and Data-Efficient Methods in Medical Image Segmentation
Dubey, A. K., Singh, V. K., Sharma, K., et al. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10514-w
Depth-Aware YOLO Segmentation: Enhancing Small Object Detection via MiDaS-Based Spatial Reasoning
Islam, N. U., Shah, S. H., Biruni, K. K. A., et al. (2025) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz67716.2025.11281862
Flow-Guided Consistent Video Depth Estimation for Cross-Dataset Generalization
Jang, J., & Kim, C. (2025) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc65261.2025.11249115
“In the case of [12], which does not function as an add-on framework, we use the DPT-Large version [17].”
Robust Single-shot Structured Light 3D Imaging via Neural Feature Decoding
Li, J., Dai, Q., Li, L., Chakravarthula, P., Sun, H., Chen, B., & Chen, W. (2025) · SIGGRAPH Asia Conference Papers · doi:10.1145/3757377.3763924
“It first extracts multi-scale image features using a Vision Transformer (ViT) [Oquab et al. 2024], then aggregates them using a DPT-style decoder [Ranftl et al. 2021].”
Yang, C., Lu, Y., Qiu, Y., & Wang, Y. (2024) · Optical Engineering · doi:10.1117/1.oe.63.8.088103
Hayes, S., Sistu, G., & Eising, C. (2025) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2025.3563677
“First, not all models provide metric depth; some instead produce absolute depth, which is not suitable for our purposes [46], [47], [48].”
Few-Shot Image Recognition Based on Multi-scale Feature Fusion of CNN-ViT Hybrid Architecture
Wang, P., Deng, X., & Sun, L. (2025) · International Conference on Electronic Information Technology (EIT) · doi:10.1109/eit67313.2025.11232179
SaLon3R: Structure-Aware Long-Term Feedforward 3D Reconstruction from Unposed Images
Guo, J., Guan, T., Dong, W., et al. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02965-2
Cen, Z., Guo, N., Guo, Y., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37255-0_23
Yu, K., Jiang, C., Kuang, H., et al. (2026) · Machine Vision and Applications · doi:10.1007/s00138-026-01929-3
“Early CNN-based methods directly regressed dense depth maps from monocular cues [15], while later self-supervised and transformer-based approaches improved generalization by exploiting stereo consistency, mixed training datasets, and stronger dense-prediction backbones [16–18].”
Metadata
- Authors
- Ranftl, R., Bochkovskiy, A., & Koltun, V.
- Affiliations
- Intel Labs
- Intel (United States) (US)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 12159-12168
- OpenAlex
- W3136635488
- Cited by
- 2,373
- References
- 88
- Citations by year
- 2026: 480 · 2025: 607 · 2024: 586 · 2023: 431 · 2022: 225 · 2021: 44
- FWCI
- 104.94
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- TIB Data Manager
- IEEE/CVF International Conference on Computer Vision (ICCV)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- vision transformer, dense prediction, monocular depth estimation, semantic segmentation, convolutional decoder, global receptive field, Pascal Context, NYUv2, KITTI, ADE20K
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