mlsanityData to 7 Oct 2026

Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation

arXiv 2210.02009 ↗First posted 5 Oct 2022cs.CVcited by 16

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Abstract (arXiv)

Depth map estimation from images is an important task in robotic systems. Existing methods can be categorized into two groups including multi-view stereo and monocular depth estimation. The former requires cameras to have large overlapping areas and sufficient baseline between cameras, while the latter that processes each image independently can hardly guarantee the structure consistency between cameras. In this paper, we propose a novel multi-camera collaborative depth prediction method that does not require large overlapping areas while maintaining structure consistency between cameras. Specifically, we formulate the depth estimation as a weighted combination of depth basis, in which the weights are updated iteratively by a refinement network driven by the proposed consistency loss. During the iterative update, the results of depth estimation are compared across cameras and the information of overlapping areas is propagated to the whole depth maps with the help of basis formulation. Experimental results on DDAD and NuScenes datasets demonstrate the superior performance of our method.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 31 of its 42 references that are in the map. First posted on arXiv 5 Oct 2022.

Builds on

31 of 42 references.

References · 31

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  4. 2021Vision Transformers for Dense Predictioncited by 1,789
  5. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
  6. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  7. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 819
  8. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 778
  9. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 721
  10. 2020AdaBins: Depth Estimation using Adaptive Binscited by 596
  11. 20193D Packing for Self-Supervised Monocular Depth Estimationcited by 437
  12. 2019Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matchingcited by 414
19 more
  1. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  2. 2019Enforcing geometric constraints of virtual normal for depth predictioncited by 233
  3. 2020Learning to Recover 3D Scene Shape from a Single Imagecited by 214
  4. 2021The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depthcited by 190
  5. 2015Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fieldscited by 169
  6. 2017A Survey of Structure from Motioncited by 157
  7. 2017Learning Depth from Monocular Videos using Direct Methodscited by 154
  8. 2019Cost Volume Pyramid Based Depth Inference for Multi-View Stereocited by 154
  9. 2018CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAMcited by 151
  10. 2020HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matchingcited by 120
  11. 2022SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimationcited by 76
  12. 2019Normal Assisted Stereo Depth Estimationcited by 49
  13. 2021Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth Predictioncited by 48
  14. 2021Full Surround Monodepth from Multiple Camerascited by 41
  15. 2019Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistencycited by 40
  16. 2020Bi3D: Stereo Depth Estimation via Binary Classificationscited by 34
  17. 2018Learning Depth from Single Images with Deep Neural Network Embedding Focal Lengthcited by 25
  18. 2021Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Datacited by 3
  19. 2021Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated Scenescited by 2

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Citing papers per year: 2023: 8; 2024: 5; 2025: 3.

16 papers.

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  1. 2023R3D3: Dense 3D Reconstruction of Dynamic Scenes from Multiple Camerascited by 39
  2. 2023OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environmentscited by 39
  3. 2025MoRE: Unlocking Scalability in Reinforcement Learning for Quadruped Vision-Language-Action Modelscited by 27
  4. 2025Sparse2DGS: Geometry-Prioritized Gaussian Splatting for Surface Reconstruction from Sparse Viewscited by 25
  5. 2023Diffusion-Augmented Depth Prediction with Sparse Annotationscited by 8
  6. 2023RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completioncited by 7
  7. 2024Towards Cross-View-Consistent Self-Supervised Surround Depth Estimationcited by 7
  8. 2023Adjacent-view Transformers for Supervised Surround-view Depth Estimationcited by 6
  9. 2024SDGE: Stereo Guided Depth Estimation for 360^circ Camera Setscited by 5
  10. 2024Towards Scale-Aware Full Surround Monodepth with Transformerscited by 5
  11. 2023Robust Self-Supervised Extrinsic Self-Calibrationcited by 4
  12. 2024Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarioscited by 4
4 more
  1. 2023BEVScope: Enhancing Self-Supervised Depth Estimation Leveraging Bird's-Eye-View in Dynamic Scenarioscited by 3
  2. 2023Multi-Frame Self-Supervised Depth Estimation with Multi-Scale Feature Fusion in Dynamic Scenescited by 2
  3. 2024Adaptive Stereo Depth Estimation with Multi-Spectral Images Across All Lighting Conditionscited by 2
  4. 2025DVLO4D: Deep Visual-Lidar Odometry with Sparse Spatial-temporal Fusioncited by 2