mlsanityData to 14 Oct 2026

FusionDepth: Complement Self-Supervised Monocular Depth Estimation with Cost Volume

arXiv 2305.06036 ↗First posted 10 May 2023cs.CVcited by 0

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Abstract (arXiv)

Multi-view stereo depth estimation based on cost volume usually works better than self-supervised monocular depth estimation except for moving objects and low-textured surfaces. So in this paper, we propose a multi-frame depth estimation framework which monocular depth can be refined continuously by multi-frame sequential constraints, leveraging a Bayesian fusion layer within several iterations. Both monocular and multi-view networks can be trained with no depth supervision. Our method also enhances the interpretability when combining monocular estimation with multi-view cost volume. Detailed experiments show that our method surpasses state-of-the-art unsupervised methods utilizing single or multiple frames at test time on KITTI benchmark.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 27 references that are in the map. First posted on arXiv 10 May 2023.

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22 of 27 references.

References · 22

  1. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  2. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  3. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  4. 2018MVSNet: Depth Inference for Unstructured Multi-view Stereocited by 788
  5. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 782
  6. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 723
  7. 2019Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matchingcited by 417
  8. 2017Sparsity Invariant CNNscited by 326
  9. 2019Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Videocited by 244
  10. 2020D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometrycited by 206
  11. 2021The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depthcited by 190
  12. 2020On the uncertainty of self-supervised monocular depth estimationcited by 182
10 more
  1. 2018DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistencycited by 148
  2. 2018Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flowcited by 135
  3. 2019Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Cameracited by 130
  4. 2020Visibility-aware Multi-view Stereo Networkcited by 98
  5. 2020Don't Forget The Past: Recurrent Depth Estimation from Monocular Videocited by 71
  6. 2019Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depthcited by 50
  7. 2020Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fieldscited by 40
  8. 2020M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Networkcited by 29
  9. 2021DF-VO: What Should Be Learnt for Visual Odometry?cited by 22
  10. 2021Generalizing to the Open World: Deep Visual Odometry with Online Adaptationcited by 19

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