SUN RGB-D: A RGB-D scene understanding benchmark suite

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684

The pascal visual object classes (VOC) challenge

Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599

Object Detection with Discriminatively Trained Part-Based Models

Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853

Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

Oliva, A., & Torralba, A. B. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011139631724 · Cited by 6,321

Indoor segmentation and support inference from rgbd images

Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760

3D ShapeNets: A deep representation for volumetric shapes

Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., & Xiao, J. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298801 · Cited by 5,413

A benchmark for the evaluation of RGB-D SLAM systems

Sturm, J., Engelhard, N., Endres, F., Burgard, W., & Cremers, D. (2012) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2012.6385773 · Cited by 4,230

LabelMe: A Database and Web-Based Tool for Image Annotation

Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210

A Closer Look at Invariances in Self-supervised Pre-training for 3D Vision

Li, L., & Heizmann, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20056-4_38 · Cited by 22

Multi-scale fusion for RGB-D indoor semantic segmentation

Jiang, S., Xu, Y., Li, D., & Fan, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-24836-9 · Cited by 20

Object Pose Estimation using Mid-level Visual Representations

Nejatishahidin, N., Fayyazsanavi, P., & Košecká, J. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981452 · Cited by 20

CrossRectify: Leveraging disagreement for semi-supervised object detection

Ma, C., Pan, X., Ye, Q., Tang, F., Dong, W., & Xu, C. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.109280 · Cited by 20

EFDCNet: Encoding fusion and decoding correction network for RGB-D indoor semantic segmentation

Chen, J., Li, G., Zhang, Z., & Zeng, D. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104892 · Cited by 20

AMFD: Distillation via Adaptive Multimodal Fusion for Multispectral Pedestrian Detection

Chen, Z., Qian, Y., Yang, X., Wang, C., & Yang, M. Z. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3604937 · Cited by 20

Learning a deeply supervised multi-modal RGB-D embedding for semantic scene and object category recognition

Zaki, H. F. M., Faisal Shafait, B., & Mian, A. S. (2017) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2017.02.008 · Cited by 20

Hierarchical Image Segmentation Ensemble for Objectness in RGB-D Images

Wang, H., Huang, D., Jia, K., & Wang, Y. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2776220 · Cited by 20

RGB-D Scene Classification via Multi-modal Feature Learning

Cai, Z., & Shao, L. (2018) · Cognitive Computation · doi:10.1007/s12559-018-9580-y · Cited by 19

Scene Recognition and Object Detection in a Unified Convolutional Neural Network on a Mobile Manipulator

Sun, H., Meng, Z., Tao, P. Y., & Ang, M. H. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460535 · Cited by 20

Structural Deep Metric Learning for Room Layout Estimation

Zheng, W., Lu, J., & Zhou, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58523-5_43 · Cited by 20

An Extensive Study and Comparison of the Various Approaches to Object Detection using Deep Learning

Subbiah, U., Kumar, D. K., Thangavel, S. K., & Parameswaran, L. (2020) · International Conference on Smart Systems and Inventive Technology (ICSSIT) · doi:10.1109/icssit48917.2020.9214185 · Cited by 21

Improving Fully Convolution Network for Semantic SegmentationPreprint

Shuai, B., Liu, T., & Wang, G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.08986 · Cited by 20

Stereo and ToF data fusion by learning from synthetic data

Agresti, G., Minto, L., Marin, G., & Zanuttigh, P. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.11.006 · Cited by 19

Near-Infrared Image Guided Reflection Removal

Hong, Y., Lyu, Y., Li, S., & Shi, B. (2020) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme46284.2020.9102937 · Cited by 19

Indoor Scene Recognition in 3D

Huang, S., Usvyatsov, M., & Konrad Schindler, B. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341580 · Cited by 19

Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image

Yang, C., Zheng, J., Dai, X., Tang, R., Ma, Y., & Yuan, X. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00031 · Cited by 19

Semi-Supervised 3d Object Detection Via Adaptive Pseudo-Labeling

Xu, H., Liu, F., Zhou, Q., Hao, J., Cao, Z., Feng, Z., & Ma, L. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506421 · Cited by 19

Learning an augmentation strategy for sparse datasets

Arantes, R. B., Vogiatzis, G., & Faria, D. R. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104338 · Cited by 19

Static-Dynamic Co-teaching for Class-Incremental 3D Object Detection

Zhao, N., & Lee, G. H. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20254 · Cited by 19

Unified DeepLabV3+ for Semi-Dark Image Semantic Segmentation

Memon, M. M., Hashmani, M. A., Junejo, A. Z., Rizvi, S. S. H., & Raza, K. (2022) · Sensors · doi:10.3390/s22145312 · Cited by 21

The Bangkok Urbanscapes Dataset for Semantic Urban Scene Understanding Using Enhanced Encoder-Decoder With Atrous Depthwise Separable A1 Convolutional Neural Networks

Thitisiriwech, K., Panboonyuen, T., Kantavat, P., Iwahori, Y., & Kijsirikul, B. (2022) · IEEE Access · doi:10.1109/access.2022.3176712 · Cited by 19

Human Activity Recognition for Assisted Living Based on Scene Understanding

Achirei, Ș.-D., Heghea, M.-C., Lupu, R. G., & Manta, V. (2022) · Applied Sciences · doi:10.3390/app122110743 · Cited by 19

Learning 3D Object Shape and Layout without 3D Supervision

Gkioxari, G., Ravi, N., & Johnson, J. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00174 · Cited by 19

2D vs. 3D LiDAR-based Person Detection on Mobile Robots

Jia, D. P., Hermans, A., & Leibe, B. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981519 · Cited by 19

Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud

Chen, L., Chang, J., Xu, J., & Yang, Z. (2023) · Applied Sciences · doi:10.3390/app13031688 · Cited by 20

SRRM: Semantic Region Relation Model for Indoor Scene Recognition

Song, C., & Ma, X. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191605 · Cited by 20

AShapeFormer : Semantics-Guided Object-Level Active Shape Encoding for 3D Object Detection via Transformers

Li, Z., Yu, H., Yang, Z., Chen, T., & Akhtar, N. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00104 · Cited by 21

DQS3D: Densely-matched Quantization-aware Semi-supervised 3D Detection

Gao, H.-A., Tian, B., Li, P., Zhao, H., & Zhou, G. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02002 · Cited by 19

A deep learning-based global and segmentation-based semantic feature fusion approach for indoor scene classification

Pereira, R., Barros, T., Garrote, L., Lopes, A., & Nunes, U. (2024) · Pattern Recognition Letters · doi:10.1016/j.patrec.2024.01.022 · Cited by 19

ActiveAnno3D - An Active Learning Framework for Multi-Modal 3D Object Detection

Ghita, A., Antoniussen, B., Zimmer, W. M. X., Greer, R., Creß, C., Møgelmose, A., Trivedi, M. M., & Knoll, A. (2024) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55156.2024.10588452 · Cited by 19

MSHP3D: Multi-stage cross-modal fusion based on Hybrid Perception for indoor 3D object detection

Jiang, X., Wang, D., Bi, K., Wang, S., & Zhang, M. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102591 · Cited by 19

Joint 2-D–3-D Traffic Sign Landmark Data Set for Geo-Localization Using Mobile Laser Scanning Data

You, C., Wen, C., Wang, C., Li, J., & Habib, A. F. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2868168 · Cited by 20

Thinking Outside the Box: Generation of Unconstrained 3D Room Layouts

Howard-Jenkins, H., Li, S., & Prisacariu, V. A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20887-5_27 · Cited by 19

DSPU: A 281.6mW Real-Time Depth Signal Processing Unit for Deep Learning-Based Dense RGB-D Data Acquisition with Depth Fusion and 3D Bounding Box Extraction in Mobile Platforms

Im, D., Park, G., Li, Z., Ryu, J., Kang, S., Han, D., Lee, J., & Yoo, H. (2022) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc42614.2022.9731699 · Cited by 19

OmniSCV: An Omnidirectional Synthetic Image Generator for Computer Vision

Berenguel‐Baeta, B., Bermúdez-Cameo, J., & Guerrero, J. J. (2020) · Sensors · doi:10.3390/s20072066 · Cited by 19

Deep Learning-Based Point Cloud Compression: An In-Depth Survey and Benchmark

Gao, W., Xie, L., Fan, S., Li, G., Liu, S., & Gao, W. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3594355 · Cited by 18

Iterative contextual and adaptive strategies for enhanced monocular depth estimation

Umirzakova, S., Mardieva, S., Muksimova, S., Baltayev, J., & Cho, Y. I. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111898 · Cited by 18

Improved U-Net-Based Novel Segmentation Algorithm for Underwater Mineral Image

Wang, H., Dong, L., Song, W. G., Zhao, X., Xia, J., & Liu, T. (2021) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.023994 · Cited by 18

Scale-aware network with modality-awareness for RGB-D indoor semantic segmentation

Zhou, F., Lai, Y., Rosin, P. L., Zhang, F., & Hu, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.025 · Cited by 18

Reflection Removal With NIR and RGB Image Feature Fusion

Hong, Y., Lyu, Y., Li, S., Cao, G., & Shi, B. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3217446 · Cited by 18

PointNorm: Dual Normalization is All You Need for Point Cloud Analysis

Zheng, S., Pan, J., Lu, C., & Gupta, G. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191312 · Cited by 18

ModelNet-O: A large-scale synthetic dataset for occlusion-aware point cloud classification

Fang, Z., Li, X., Li, X., Zhao, S., & Liu, M. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104060 · Cited by 18

Exploiting Diffusion Prior for Generalizable Dense Prediction

Lee, H.-Y., Tseng, H.-Y., Lee, H.-Y., & Yang, M. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00751 · Cited by 30

Multimodal Low-light Image Enhancement with Depth Information

Wang, Z. R., Li, D., Li, G., Zhang, Z., & Jiang, R. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680741 · Cited by 18

Object detection via deeply exploiting depth information

Hou, S., Wang, Z., & Wu, F. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.01.055 · Cited by 18

Learning to Generate Unambiguous Spatial Referring Expressions for Real-World Environments

Doğan, F. I., Kalkan, S., & Leite, I. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968510 · Cited by 18

Globally Optimal Inlier Set Maximization for Atlanta World Understanding

Joo, K., Oh, T.-H., Kweon, I. S., & Bazin, J. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2909863 · Cited by 18

RGB-D Scene Recognition via Spatial-Related Multi-Modal Feature Learning

Xiong, Z., Yuan, Y., & Wang, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2932080 · Cited by 20

DISCOMAN: Dataset of Indoor SCenes for Odometry, Mapping And Navigation

Kirsanov, P., Gaskarov, A., Konokhov, F., Sofiiuk, K., Vorontsova, A., Slinko, I., Zhukov, D., Bykov, S. N., Barinova, O., & Konushin, A. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967921 · Cited by 18

Shallow2Deep: Indoor scene modeling by single image understanding

Nie, Y., Guo, S., Chang, J. M., Han, X., Huang, J., Hu, S., & Zhang, J. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107271 · Cited by 18

Deep-Learning based Global and Semantic Feature Fusion for Indoor Scene Classification

Pereira, R., Gonçalves, N., Garrote, L., Barros, T., Lopes, A., & Nunes, U. (2020) · IEEE International Conference on Autonomous Robot Systems and Competitions · doi:10.1109/icarsc49921.2020.9096068 · Cited by 18

Hierarchical Object Relationship Constrained Monocular Depth Estimation.

Li, S., Shi, J., Song, W., Hao, A., & Qin, H. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108116 · Cited by 18

3DFacilities: Annotated 3D Reconstructions of Building Facilities

Czerniawski, T., & Leite, F. L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91635-4_10 · Cited by 18

Sparse2Dense: From Direct Sparse Odometry to Dense 3-D Reconstruction

Tang, J., Folkesson, J. B., & Jensfelt, P. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2891433 · Cited by 18

Cross Modal Distillation for Supervision TransferPreprint

Gupta, S., Hoffman, J., & Malik, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.00448 · Cited by 34

UASOL, a large-scale high-resolution outdoor stereo dataset

Bauer, Z., Gomez‐Donoso, F., Cruz, E., Orts‐Escolano, S., & Cazorla, M. (2019) · Scientific Data · doi:10.1038/s41597-019-0168-5 · Cited by 18

A real 3d embodied dataset for robotic active visual learning

Zhao, Q., Zhang, L., Wu, L., Qiao, H., & Liu, Z. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3157028 · Cited by 17

WorldScore: A Unified Evaluation Benchmark for World Generation

Duan, H., Yu, H., Chen, S., Fei–Fei, L., & Wu, J. (2025) · 2025 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02573 · Cited by 17

The methods for improving large-scale multi-view clustering efficiency: a survey

Yang, Z., & Tan, Y. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10785-4 · Cited by 17

Visual Grounding in 2D and 3D: A unified perspective and survey

Guo, K., Huang, Y., Jia, T., Song, X., Sun, S., Wei, H., Han, X.-F., Huang, S., Strisciuglio, N., & Li, S. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103625 · Cited by 19

Central Attention and a Dual Path Convolutional Neural Network in Real-World Tree Species Recognition

Chung, Y., Chou, C.-A., & Li, C.-Y. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18030961 · Cited by 17

Multi-modal feature fusion for 3D object detection in the production workshop

Hou, R. L., Chen, G., Han, Y., Tang, Z., & Ru, Q. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.108245 · Cited by 17

CI-Net: a joint depth estimation and semantic segmentation network using contextual information

Gao, T., Wei, W., Cai, Z., Fan, Z., Xie, S. Q., Wang, X., & Yu, Q. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03401-x · Cited by 22

Entanglement inspired approach for determining the preeminent arrangement of static cameras in a multi-view computer vision system

Kumar, A., Mai, N. N., Guo, S., & Han, L. (2022) · The Visual Computer · doi:10.1007/s00371-022-02497-z · Cited by 17

Nearest Neighbors Meet Deep Neural Networks for Point Cloud Analysis

Zhang, R., Wang, L., Guo, Z., & Shi, J. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00130 · Cited by 17

Attentional Graph Convolutional Network for Structure-Aware Audiovisual Scene Classification

Zhou, L., Zhou, Y., Qi, X., Hu, J., Lam, T. L., & Xu, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3260282 · Cited by 17

Semantic-Context Graph Network for Point-Based 3D Object Detection

Dong, S., Kong, X., Pan, X., Tang, F., Li, W., Chang, Y., & Dong, W. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3271318 · Cited by 18

Implicit Ray Transformers for Multiview Remote Sensing Image Segmentation

Qi, Z., Chen, H., Liu, C., Shi, Z., & Zou, Z. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3285659 · Cited by 17

PanopticNDT: Efficient and Robust Panoptic Mapping

Seichter, D., Stephan, B., Fischedick, S. B., Müller, S., Rabes, L., & Groß, H. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342137 · Cited by 17

AGG-Net: Attention Guided Gated-convolutional Network for Depth Image Completion

Chen, D., Huang, T., Song, Z., Deng, S., & Jia, T. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00813 · Cited by 17

SpaceBlender: Creating Context-Rich Collaborative Spaces Through Generative 3D Scene Blending

Numan, N., Rajaram, S., Kumaravel, B. T., Marquardt, N., & Wilson, A. D. (2024) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3654777.3676361 · Cited by 19

Color and Depth Sensing Sensor Technologies for Robotics and Machine Vision

Ali, S., & Pandey, A. K. (2019) · Springer eBooks · doi:10.1007/978-3-030-22587-2_3 · Cited by 17

Zero-shot Deep Domain Adaptation with Common Representation Learning

Kutbi, M., Peng, K., & Wu, Z. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3061204 · Cited by 16

Fast Computation of Content-Sensitive Superpixels and Supervoxels Using Q-Distances

Ye, Z., Yi, R., Yu, M., Liu, Y., & He, Y. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00387 · Cited by 17

Gated Feedback Refinement Network for Coarse-to-Fine Dense Semantic Image LabelingPreprint

Islam, M. A., Rochan, M., Naha, S., Bruce, N. D. B., & Wang, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.11266 · Cited by 17

OctNetFusion: Learning Depth Fusion from DataPreprint

Riegler, G., Ulusoy, A. O., Bischof, H., & Geiger, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01047 · Cited by 15

Qwen3-VL Technical Report

Bai, S., Cai, Y., Chen, R., Chen, K., Chen, X., Cheng, Z., Deng, L., Ding, W., Gao, C., Ge, C., et al. (2025) · arXiv · doi:10.48550/arxiv.2511.21631 · Cited by 27

Monocular depth estimation via a detail semantic collaborative network for indoor scenes

Song, W. Q., Cui, X., Xie, Y., Wang, G., & Ma, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-96024-4 · Cited by 16

RFNet: Reverse Fusion Network With Attention Mechanism for RGB-D Indoor Scene Understanding

Zhou, W., Lv, S., Lei, J., Luo, T., & Yu, L. (2022) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3160720 · Cited by 16

Cross-Modal Knowledge Transfer Without Task-Relevant Source Data

Ahmed, S. M., Lohit, S., Peng, K., Jones, M., & Roy–Chowdhury, A. K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19830-4_7 · Cited by 16

Monocular Depth Estimation: a Review of the 2022 State of the Art

Ehret, T. (2023) · Image Processing On Line · doi:10.5201/ipol.2023.459 · Cited by 20

Single Image Depth Prediction Made Better: A Multivariate Gaussian Take

Liu, C., Kumar, S., Gu, S., Timofte, R., & Gool, L. V. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01664 · Cited by 18

Objformer: Boosting 3D object detection via instance-wise interaction

Tao, M., Zhao, C., Tang, M., & Wang, J. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110061 · Cited by 16

MENet: Multi-Modal Mapping Enhancement Network for 3D Object Detection in Autonomous Driving

Liu, M., Chen, Y., Xie, J., Zhu, Y., Zhang, Y., Yao, L., Bing, Z., Zhuang, G., Huang, K., & Zhou, J. T. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3387398 · Cited by 17

RGB Guided ToF Imaging System: A Survey of Deep Learning-Based Methods

Qiao, X., Poggi, M., Deng, P., Hao, W., Ge, C., & Mattoccia, S. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02089-5 · Cited by 20

Attention-based fusion network for RGB-D semantic segmentation

Li, Z., Guo, C., Zhan, J., & Deng, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128371 · Cited by 16

Viewpoint invariant semantic object and scene categorization with RGB-D sensors

Zaki, H. F. M., Faisal Shafait, B., & Mian, A. S. (2018) · Autonomous Robots · doi:10.1007/s10514-018-9776-8 · Cited by 15

SUN-Spot: An RGB-D Dataset With Spatial Referring Expressions

Mauceri, C., Palmer, M. S., & Heckman, C. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00236 · Cited by 16

A Compressed and Accelerated SegNet for Plant Leaf Disease Segmentation: A Differential Evolution Based Approach

Agarwal, M., Gupta, S. K., & Biswas, K. K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-75768-7_22 · Cited by 16

ARnnotate: An Augmented Reality Interface for Collecting Custom Dataset of 3D Hand-Object Interaction Pose Estimation

Qian, X., He, F., Hu, X., Wang, T., & Karthik Ramani, A. (2022) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3526113.3545663 · Cited by 16

Multimodal Detection of Unknown Objects on Roads for Autonomous Driving

Bogdoll, D., Eisen, E., Nitsche, M., Scheib, C., & Zöllner, J. M. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945211 · Cited by 16

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Agostinelli, A., Pándy, M., Uijlings, J., Mensink, T., & Ferrari, V. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19830-4_18 · Cited by 23

Multi-modal panoramic 3D outdoor datasets for place categorization

Jung, H., Oto, Y., Mozos, O. M., Iwashita, Y., & Kurazume, R. (2016) · 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759669 · Cited by 16

Depth Images Could Tell us More: Enhancing Depth Discriminability for RGB-D Scene Recognition

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Oriented Point Sampling for Plane Detection in Unorganized Point Clouds

Sun, B., & Mordohai, P. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793487 · Cited by 16

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Shen, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12683 · Cited by 20

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Ye, J., Batra, D., Das, A., & Wijmans, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.04112 · Cited by 16

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Pedro, D., Mora, A., Matos-Carvalho, J. P., Azevedo, F., & Fonseca, J. (2020) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-45124-0_5 · Cited by 16

SceneNN: A Scene Meshes Dataset with aNNotations

Hua, B., Pham, Q.-H., Nguyen, D. T., Tran, M.-K., Yu, L.-F., & Yeung, S.-K. (2016) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.18 · Cited by 347

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Chang, A., Dai, A., Funkhouser, T., Halber, M., Niebner, M., Savva, M., Song, S., Zeng, A., & Zhang, Y. (2017) · 2017 International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00081 · Cited by 1,858

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Semantic 3D Scene Understanding in RGB-D Scans.

Nießner, M. (2020)