Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
Open accessAbstract
For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real images. We address this challenge by introducing Hypersim, a photorealistic synthetic dataset for holistic indoor scene understanding. To create our dataset, we leverage a large repository of synthetic scenes created by professional artists, and we generate 77,400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry. Our dataset: (1) relies exclusively on publicly available 3D assets; (2) includes complete scene geometry, material information, and lighting information for every scene; (3) includes dense per-pixel semantic instance segmentations and complete camera information for every image; and (4) factors every image into diffuse reflectance, diffuse illumination, and a non-diffuse residual term that captures view-dependent lighting effects.We analyze our dataset at the level of scenes, objects, and pixels, and we analyze costs in terms of money, computation time, and annotation effort. Remarkably, we find that it is possible to generate our entire dataset from scratch, for roughly half the cost of training a popular open-source natural language processing model. We also evaluate sim-to-real transfer performance on two real-world scene understanding tasks – semantic segmentation and 3D shape prediction – where we find that pre-training on our dataset significantly improves performance on both tasks, and achieves state-of-the-art performance on the most challenging Pix3D test set. All of our rendered image data, as well as all the code we used to generate our dataset and perform our experiments, is available online.
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“ADE20K[117] BBBC038v1[12] Cityscapes[25] DOORS[80] DRAM[24] EgoHOS[113] GTEA[34,63] Hypersim[86] IBD[17] iShape[111] LVIS[44] NDD20[100] NDISPark[22,23] OVIS[81] PPDLS[74] Plittersdorf[46] STREETS[91] TimberSeg[38] TrashCan[52] VISOR[28,27] WoodScape[112] PIDRay[104] ZeroWaste-f[6] Figure 8: Samples from the 23 divers…”
BackgroundInfluentialDepth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., & Zhao, H. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00987 · Cited by 1,296 · see citing works
Data augmentation: A comprehensive survey of modern approaches
Mumuni, A., & Mumuni, F. (2022) · Array · doi:10.1016/j.array.2022.100258 · Cited by 891 · see citing works
Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Ke, B., Obukhov, A., Huang, S., Metzger, N., Daudt, R. C., & Schindler, K. (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/cvpr52733.2024.00907 · Cited by 444 · see citing works
MultiMAE: Multi-modal Multi-task Masked Autoencoders
Bachmann, R., Mizrahi, D., Atanov, A., & Zamir, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19836-6_20 · Cited by 268 · see citing works
Hu, M., Yin, W., Zhang, C., Cai, Z., Long, X., Chen, H., Wang, K., Yu, G., Shen, C., & Shen, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3444912 · Cited by 279 · see citing works
Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans
Eftekhar, A., (Sasha), A. S., Malik, J., & Zamir, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01061 · Cited by 261 · see citing works
ABO: Dataset and Benchmarks for Real-World 3D Object Understanding
Collins, J., Goel, S., Deng, K., Luthra, A., Xu, L. L., Gündoğdu, E., Zhang, X., Vicente, T. F. Y., Dideriksen, T. L., Arora, H., et al. (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/cvpr52688.2022.02045 · Cited by 162 · see citing works
Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations
Ahmadyan, A., Zhang, L., Ablavatski, A., Wei, J., & Grundmann, M. (2021) · 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/cvpr46437.2021.00773 · Cited by 147 · see citing works
Panoptic Lifting for 3D Scene Understanding with Neural Fields
Siddiqui, Y., Porzi, L., Bulò, S. R., Muller, N. E., Nießner, M., Dai, A., & Kontschieder, P. (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/cvpr52729.2023.00873 · Cited by 132 · see citing works
A Survey of Synthetic Data Augmentation Methods in Machine Vision
Mumuni, A., Mumuni, F., & Gerrar, N. K. (2024) · Machine Intelligence Research · doi:10.1007/s11633-022-1411-7 · Cited by 148 · see citing works
Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild
Brazil, G., Kumar, A., Straub, J., Ravi, N., Johnson, J., & Gkioxari, G. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01264 · Cited by 111 · see citing works
Self-citationDiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion Models
Wynn, J., & Turmukhambetov, D. (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/cvpr52729.2023.00407 · Cited by 111 · see citing works
DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients
Pautrat, R., Baráth, D., Larsson, V., Oswald, M. R., & Pollefeys, M. (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/cvpr52729.2023.01662 · Cited by 93 · see citing works
3D Common Corruptions and Data Augmentation
Kar, O. F., Yeo, T., Atanov, A., & Zamir, A. (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/cvpr52688.2022.01839 · Cited by 90 · see citing works
Ditto: Building Digital Twins of Articulated Objects from Interaction
Jiang, Z., Hsu, C.-C., & Zhu, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00553 · Cited by 92 · see citing works
Infinite Photorealistic Worlds Using Procedural Generation
Raistrick, A., Lipson, L., Ma, Z., Mei, L., Wang, M., Zuo, Y., Kayan, K., Wen, H., Han, B., Wang, Y., et al. (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/cvpr52729.2023.01215 · Cited by 89 · see citing works
Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action
Lu, J., Clark, C., Lee, S.-H., Zhang, Z., Khosla, S., Marten, R., Hoiem, D., & Kembhavi, A. (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/cvpr52733.2024.02497 · Cited by 98 · see citing works
EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AI
Wang, T., Mao, X., Zhu, C., Xu, R., Lyu, R., Li, P., Chen, X., Zhang, W., Chen, K., Xue, T., et al. (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/cvpr52733.2024.01868 · Cited by 89 · see citing works
Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
Yang, J., (Sasha), A. S., Liang, K. J., Henaff, M., Tang, H., Cao, A., Chai, J. Y., Meier, F., & Feiszli, M. (2025) · 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/cvpr52734.2025.02042 · Cited by 89 · see citing works
OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets
Li, Z., Yu, T.-W., Sang, S., Wang, S., Song, M. J., Liu, Y., Yeh, Y.-Y., Zhu, R., Gundavarapu, N. B., Shi, J. D., et al. (2021) · 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/cvpr46437.2021.00711 · Cited by 77 · see citing works
Toward Practical Monocular Indoor Depth Estimation
Wu, C.-Y., Wang, J., Hall, M., Neumann, U., & Su, S. (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/cvpr52688.2022.00379 · Cited by 75 · see citing works
Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine Perception
Pan, X., Charron, N., Yang, Y., Peters, S., Whelan, T. J., Chen, K., Parkhi, O., Newcombe, R., & Ren, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01842 · Cited by 76 · see citing works
GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image
Fu, X. Y., Yin, W., Hu, M., Wang, K., Ma, Y., Tan, P., Shen, S., Lin, D., & Long, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72670-5_14 · Cited by 78 · see citing works
Haruna, Y., Qin, S., Chukkol, A. H. A., Yusuf, A. A., Bello, I., & Lawan, A. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110057 · Cited by 95 · see citing works
Multiview Compressive Coding for 3D Reconstruction
Wu, C.-Y., Johnson, J., Malik, J., Feichtenhofer, C., & Gkioxari, G. (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/cvpr52729.2023.00875 · Cited by 66 · see citing works
Rani, A., Ortíz-Arroyo, D., & Durdevic, P. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102575 · Cited by 67 · see citing works
ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation
Patni, S., Agarwal, A., & Arora, C. (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/cvpr52733.2024.02672 · Cited by 65 · see citing works
Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments
Seichter, D., Fischedick, S. B., Köhler, M., & Grob, H.-M. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892852 · Cited by 60 · see citing works
NeRF-RPN: A general framework for object detection in NeRFs
Hu, B., Huang, J., Liu, Y., Tai, Y., & Tang, C. (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/cvpr52729.2023.02253 · Cited by 61 · see citing works
Rethinking Inductive Biases for Surface Normal Estimation
Bae, G., & Davison, A. J. (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/cvpr52733.2024.00911 · Cited by 62 · see citing works
SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation With Fine-Grained Geometry
Gao, L., Sun, J.-M., Mo, K., Lai, Y., Guibas, L. J., & Yang, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3237577 · Cited by 57 · see citing works
Lopes, A., de Souza, R. M., & Pedrini, H. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103489 · Cited by 52 · see citing works
FWD: Real-time Novel View Synthesis with Forward Warping and Depth
Cao, A., Rockwell, C., & 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/cvpr52688.2022.01526 · Cited by 49 · see citing works
IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis
Ye, W., Chen, S., Bao, C. G., Bao, H., Pollefeys, M., Cui, Z., & Zhang, G. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00038 · Cited by 49 · see citing works
IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor Scenes
Zhu, R., Li, Z., Matai, J., Porikli, F., & Chandraker, M. K. (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/cvpr52688.2022.00284 · Cited by 47 · see citing works
Intrinsic Image Decomposition via Ordinal Shading
Careaga, C., & Aksoy, Y. (2023) · ACM Transactions on Graphics · doi:10.1145/3630750 · Cited by 46 · see citing works
Reyes, M. F., Xie, Y., Yuan, X., d’Angelo, P., Kurz, F., Cerra, D., & Tian, J. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.09.013 · Cited by 43 · see citing works
Indoor Synthetic Data Generation: A Systematic Review
Schieber, H., Demir, K. C., Kleinbeck, C., Yang, S. H., & Roth, D. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103907 · Cited by 42 · see citing works
Learning Navigational Visual Representations with Semantic Map Supervision
Hong, Y., Zhou, Y., Zhang, R., Dernoncourt, F., Bui, T., Gould, S., & Tan, H. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00284 · Cited by 42 · see citing works
Multimodal fusion and vision–language models: A survey for robot vision
Han, X., Chen, S., Fu, Z., Feng, Z., Fan, L., An, D., Wang, C., Guo, L., Meng, W., Zhang, X., et al. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103652 · Cited by 41 · see citing works
“For indoor scene understanding, datasets such as Matterport3D[26], ScanNet [248], NYU Depth V2 [249], and Hypersim [250] have been pivotal.”
On 3D Reconstruction Using RGB-D Cameras
Tychola, K. A., Tsimperidis, I., & Papakostas, G. A. (2022) · Digital · doi:10.3390/digital2030022 · Cited by 41 · see citing works
AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views
Jiang, L., Mao, Y., Xu, L., Lu, T., Ren, K., Jin, Y., Xu, X., Yu, M., Pang, J., Zhao, F., et al. (2025) · ACM Transactions on Graphics · doi:10.1145/3763326 · Cited by 46 · see citing works
PMatch: Paired Masked Image Modeling for Dense Geometric Matching
Zhu, S., & Liu, X. (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/cvpr52729.2023.02098 · Cited by 41 · see citing works
Liu, S., Yu, Y., Pautrat, R., Pollefeys, M., & Larsson, 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/cvpr52729.2023.02054 · Cited by 41 · see citing works
Jia, Y., Hoyer, L., Huang, S., Wang, T., Gool, L. V., Konrad Schindler, B., & Obukhov, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72933-1_6 · Cited by 40 · see citing works
MatSynth: A Modern PBR Materials Dataset
Vecchio, G., & Deschaintre, V. (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/cvpr52733.2024.02087 · Cited by 36 · see citing works
NViSII: A Scriptable Tool for Photorealistic Image GenerationPreprint
Morrical, N., Tremblay, J., Lin, Y., Tyree, S., Birchfield, S. T., Pascucci, V., & Wald, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.13962 · Cited by 36 · see citing works
Depth-Guided Robust Point Cloud Fusion NeRF for Sparse Input Views
Guo, S., Wang, Q., Gao, Y., Xie, R., Li, L., Zhu, F. P., & Song, L. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3385360 · Cited by 37 · see citing works
ControlRoom3D: Room Generation Using Semantic Proxy Rooms
Schult, J., Tsai, S. S., Höllein, L., Wu, B., Wang, J., Ma, C., Li, K., Wang, X., Wimbauer, F., He, Z., et al. (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/cvpr52733.2024.00593 · Cited by 37 · see citing works
Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail
Bartolomei, L., Tosi, F., Poggi, M., & Mattoccia, S. (2025) · 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/cvpr52734.2025.00103 · Cited by 32 · see citing works
Real-time accident anticipation for autonomous driving through monocular depth-enhanced 3D modeling
Liao, H., Li, Y., Li, Z., Bian, Z., Lee, J., Cui, Z., Zhang, G., & Xu, C. (2024) · Accident Analysis & Prevention · doi:10.1016/j.aap.2024.107760 · Cited by 31 · see citing works
A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing
Upchurch, P., & Niu, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20074-8_26 · Cited by 31 · see citing works
Sim VQA: Exploring Simulated Environments for Visual Question Answering
Cascante-Bonilla, P., Wu, H., Wang, L., Feris, R., & Ordóñez, V. (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/cvpr52688.2022.00500 · Cited by 32 · see citing works
DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models
Liang, R., Gojčič, Ž., Ling, H., Munkberg, J., Hasselgren, J., Lin, C.-H., Gao, J., Keller, A., Vijaykumar, N., Fidler, S., et al. (2025) · 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/cvpr52734.2025.02428 · Cited by 30 · see citing works
A Smart Home Digital Twin to Support the Recognition of Activities of Daily Living
Bouchabou, D., Grosset, J., Nguyen, S. M., Lohr, C., & Puig, X. (2023) · Sensors · doi:10.3390/s23177586 · Cited by 29 · see citing works
LightIt: Illumination Modeling and Control for Diffusion Models
Kocsis, P., Philip, J., Sunkavalli, K., Nießner, M., & Hold-Geoffroy, Y. (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/cvpr52733.2024.00894 · Cited by 30 · see citing works
Monte Carlo Scene Search for 3D Scene Understanding
Hampali, S., Stekovic, S., Sarkar, S. D., Kumar, C. S., Fraundorfer, F., & Lepetit, V. (2021) · 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/cvpr46437.2021.01359 · Cited by 34 · see citing works
Neural Partitioning Pyramids for Denoising Monte Carlo Renderings
Bálint, M., Wolski, K., Myszkowski, K., Seidel, H., & Mantiuk, R. (2023) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3588432.3591562 · Cited by 27 · see citing works
Fine-Tuning Image-Conditional Diffusion Models is Easier than you Think
Garcia, G. M., Zeid, K. A., Schmidt, C., de Geus, D., Hermans, A., & Leibe, B. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00083 · Cited by 27 · see citing works
NTIRE 2025 Challenge on HR Depth From Images of Specular and Transparent Surfaces
Ramirez, P. Z., Tosi, F., Stefano, L. D., Timofte, R., Costanzino, A., Poggi, M., Salti, S., Mattoccia, S., Zhang, Z., Yang, Y., et al. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00098 · Cited by 28 · see citing works
Emotion recognition using facial expressions in an immersive virtual reality application
Chen, X., & Chen, H. (2022) · Virtual Reality · doi:10.1007/s10055-022-00720-9 · Cited by 26 · see citing works
Benchmarking neural radiance fields for autonomous robots: An overview
Ming, Y., Yang, X., Wang, W., Chen, Z., Feng, J., Xing, Y., & Zhang, G. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109685 · Cited by 26 · see citing works
Guided Co-Modulated GAN for 360° Field of View Extrapolation
Dastjerdi, M. R. K., Hold-Geoffroy, Y., Eisenmann, J., Khodadadeh, S., & Lalonde, J.-F. (2022) · International Conference on 3D Vision · doi:10.1109/3dv57658.2022.00059 · Cited by 43 · see citing works
Pointersect: Neural Rendering with Cloud-Ray Intersection
Chang, J.-H. R., Chen, W.-Y., Ranjan, A., Yi, K. M., & Tuzel, O. (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/cvpr52729.2023.00808 · Cited by 24 · see citing works
Self-citationEfficient Multi-Task Scene Analysis with RGB-D Transformers
Fischedick, S. B., Seichter, D., Schmidt, R., Rabes, L., & Groß, H. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191977 · Cited by 23 · see citing works
UniMODE: Unified Monocular 3D Object Detection
Li, Z., Xu, X., Lim, S.-N., & Zhao, H. (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/cvpr52733.2024.01567 · Cited by 22 · see citing works
All for one, and one for all: UrbanSyn Dataset, the third Musketeer of synthetic driving scenes
Gómez, J. L., da Silva, M. A. F. M., Seoane, A., Borràs, A., Noriega, M., Ros, G., Iglesias-Guitián, J. A., & López, A. M. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130038 · Cited by 27 · see citing works
Improving 3D Object Detection in Neural Radiance Fields With Channel Attention
Zhu, M., Gong, Y., Gu, D., & Tian, C. (2025) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.70045 · Cited by 21 · see citing works
Advances in Data‐Driven Analysis and Synthesis of 3D Indoor Scenes
Patil, A. G., Patil, S. G., Li, M., Fisher, M., Savva, M., & Zhang, H. (2023) · Computer Graphics Forum · doi:10.1111/cgf.14927 · Cited by 21 · see citing works
Multi-view Inverse Rendering for Large-scale Real-world Indoor Scenes
Li, Z., Wang, L., Cheng, M., Pan, C., & Yang, J. (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/cvpr52729.2023.01203 · Cited by 20 · see citing works
UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images
He, B., Li, X., Huang, B., Gu, E., Guo, W., & Wu, L. (2021) · Remote Sensing · doi:10.3390/rs13244999 · Cited by 19 · see citing works
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 · see citing works
Towards Robust Monocular Depth Estimation: A New Baseline and Benchmark
Xian, K., Cao, Z., Shen, C., & Lin, G. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-023-01979-4 · Cited by 19 · see citing works
Liu, X., Chen, J., Kao, S.-H., Tai, Y., & Tang, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72640-8_19 · Cited by 26 · see citing works
Tang, H., & Jia, K. (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/cvpr52729.2023.01531 · Cited by 20 · see citing works
Towards Zero Domain Gap: A Comprehensive Study of Realistic LiDAR Simulation for Autonomy Testing
Manivasagam, S., Bârsan, I. A., Wang, J., Yang, Z., & Urtasun, R. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00760 · Cited by 18 · see citing works
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
“Then, we source images from open-source scene datasets [8, 38, 42, 57, 62, 67, 69, 74, 98] and supplement with an online source, Unsplash [7].”
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 · see citing works
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 · see citing works
“…et al., 2014; Mao et al., 2016), ODinW-13 (Li et al., 2022), CountBench (Paiss et al., 2023), ARKitScenes (Baruch et al., 2021), Hypersim (Roberts et al., 2021), SUN RGB-D (Song et al., 2015), ERQA (Team et al., 2025), VSIBench (Yang et al., 2025b), EmbSpatial (Du et al.,…”
Sim2Real Instance-Level Style Transfer for 6D Pose Estimation
Ikeda, T., Tanishige, S., Amma, A., Sudano, M., Audren, H., & Nishiwaki, K. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981878 · Cited by 16 · see citing works
3DB: A Framework for Debugging Computer Vision ModelsPreprint
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Reference Based Sketch Extraction via Attention Mechanism
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Consistent Direct Time-of-Flight Video Depth Super-Resolution
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PLGS: Robust Panoptic Lifting With 3D Gaussian Splatting
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Hybrid Data Augmentation for Enhanced Crack Detection in Building Construction
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SYNTHETIC DATA GENERATION PIPELINE FOR GEOMETRIC DEEP LEARNING IN ARCHITECTURE
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The Second Monocular Depth Estimation Challenge
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- Authors
- Roberts, M., Ramapuram, J., Ranjan, A., Kumar, A., Bautista, M. A., Paczan, N., Webb, R., & Susskind, J. M.
- Affiliations
- Apple (United States) (US)
- Apple
- ORCID
- Ramapuram, Jason
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 10892-10902
- OpenAlex
- W3097660860
- Cited by
- 358
- References
- 95
- Citations by year
- 2026: 85 · 2025: 88 · 2024: 73 · 2023: 61 · 2022: 31 · 2021: 19 · 2020: 1
- FWCI
- 33.65
- 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)
- IEEE/CVF International Conference on Computer Vision (ICCV)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- 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
- Keywords
- synthetic dataset, indoor scene understanding, photorealistic rendering, instance segmentation, sim-to-real transfer, diffuse reflectance, scene geometry, annotation cost
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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