mlsanityData to 7 Oct 2026

DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes

arXiv 2108.05615 ↗First posted 12 Aug 2021cs.CVcited by 5

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Abstract (arXiv)

We present a novel approach for estimating depth from a monocular camera as it moves through complex and crowded indoor environments, e.g., a department store or a metro station. Our approach predicts absolute scale depth maps over the entire scene consisting of a static background and multiple moving people, by training on dynamic scenes. Since it is difficult to collect dense depth maps from crowded indoor environments, we design our training framework without requiring depths produced from depth sensing devices. Our network leverages RGB images and sparse depth maps generated from traditional 3D reconstruction methods to estimate dense depth maps. We use two constraints to handle depth for non-rigidly moving people without tracking their motion explicitly. We demonstrate that our approach offers consistent improvements over recent depth estimation methods on the NAVERLABS dataset, which includes complex and crowded scenes.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 45 of its 70 references that are in the map. First posted on arXiv 12 Aug 2021.

Builds on

45 of 70 references.

References · 45

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2017Mask R-CNNcited by 7,259
  4. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
  5. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,484
  6. 2017Matterport3D: Learning from RGB-D Data in Indoor Environmentscited by 1,354
  7. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
  8. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 819
  9. 2016FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networkscited by 780
  10. 2018MVSNet: Depth Inference for Unstructured Multi-view Stereocited by 780
  11. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 778
  12. 2018MegaDepth: Learning Single-View Depth Prediction from Internet Photoscited by 731
33 more
  1. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 721
  2. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
  3. 20193D Packing for Self-Supervised Monocular Depth Estimationcited by 437
  4. 2017Joint 2D-3D-Semantic Data for Indoor Scene Understandingcited by 404
  5. 2016Deeper Depth Prediction with Fully Convolutional Residual Networkscited by 402
  6. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 379
  7. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  8. 2018GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Posecited by 341
  9. 2019Enforcing geometric constraints of virtual normal for depth predictioncited by 233
  10. 2020Consistent Video Depth Estimationcited by 220
  11. 2018DeepV2D: Video to Depth with Differentiable Structure from Motioncited by 185
  12. 2016Single-Image Depth Perception in the Wildcited by 179
  13. 2015Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fieldscited by 169
  14. 2020Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths from a Monocular Cameracited by 166
  15. 2017A Survey of Structure from Motioncited by 157
  16. 2018Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Cameracited by 140
  17. 2019Learning the Depths of Moving People by Watching Frozen Peoplecited by 120
  18. 2019Depth Completion from Sparse LiDAR Data with Depth-Normal Constraintscited by 108
  19. 2020Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinementcited by 105
  20. 2019Web Stereo Video Supervision for Depth Prediction from Dynamic Scenescited by 73
  21. 2019Neural RGB->D Sensing: Depth and Uncertainty from a Video Cameracited by 69
  22. 2019Dense Depth Posterior (DDP) from Single Image and Sparse Rangecited by 57
  23. 2018Confidence Propagation through CNNs for Guided Sparse Depth Regressioncited by 52
  24. 2019Large-scale, real-time visual-inertial localization revisitedcited by 45
  25. 2020Benchmarking Image Retrieval for Visual Localizationcited by 42
  26. 2020P2Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimationcited by 41
  27. 2017Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistencycited by 40
  28. 2020Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving Applicationscited by 40
  29. 2021Large-scale Localization Datasets in Crowded Indoor Spacescited by 28
  30. 2018Learning Single-Image Depth from Videos using Quality Assessment Networkscited by 25
  31. 2020DenseCAvoid: Real-time Navigation in Dense Crowds using Anticipatory Behaviorscited by 25
  32. 2020SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environmentscited by 13
  33. 2019Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimationcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 3; 2026: 1.

5 papers.

  1. 2022SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learningcited by 4
  2. 2025RPG360: Robust 360 Depth Estimation with Perspective Foundation Models and Graph Optimizationcited by 4
  3. 2025MoRe: Monocular Geometry Refinement via Graph Optimization for Cross-View Consistencycited by 3
  4. 2026Wid3R: Wide Field-of-View 3D Reconstruction via Camera Model Conditioningcited by 3
  5. 2025UAV4D: Dynamic Neural Rendering of Human-Centric UAV Imagery using Gaussian Splattingcited by 2