mlsanityData to 7 Oct 2026

Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation

arXiv 2304.00152 ↗First posted 31 Mar 2023cs.CVcited by 23

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Abstract (arXiv)

We present a new loss function for joint disparity and uncertainty estimation in deep stereo matching. Our work is motivated by the need for precise uncertainty estimates and the observation that multi-task learning often leads to improved performance in all tasks. We show that this can be achieved by requiring the distribution of uncertainty to match the distribution of disparity errors via a KL divergence term in the network's loss function. A differentiable soft-histogramming technique is used to approximate the distributions so that they can be used in the loss. We experimentally assess the effectiveness of our approach and observe significant improvements in both disparity and uncertainty prediction on large datasets.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 16 of its 39 references that are in the map. First posted on arXiv 31 Mar 2023.

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16 of 39 references.

References · 16

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  4. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  5. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
  6. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 878
  7. 2018Pyramid Stereo Matching Networkcited by 486
  8. 2017End-to-End Learning of Geometry and Context for Deep Stereo Regressioncited by 359
  9. 2016Virtual Worlds as Proxy for Multi-Object Tracking Analysiscited by 340
  10. 2019GA-Net: Guided Aggregation Net for End-to-end Stereo Matchingcited by 242
  11. 2019Group-wise Correlation Stereo Networkcited by 235
  12. 2020On the uncertainty of self-supervised monocular depth estimationcited by 182
4 more
  1. 2018Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flowcited by 135
  2. 2018SegStereo: Exploiting Semantic Information for Disparity Estimationcited by 88
  3. 2021On the confidence of stereo matching in a deep-learning era: a quantitative evaluationcited by 23
  4. 2016Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learningcited by 18

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Citing papers per year: 2023: 2; 2024: 10; 2025: 7; 2026: 4.

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  1. 2025FoundationStereo: Zero-Shot Stereo Matchingcited by 168
  2. 2024Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Failcited by 45
  3. 2023MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformercited by 29
  4. 2024NARUTO: Neural Active Reconstruction from Uncertain Target Observationscited by 27
  5. 2024A Survey on Deep Stereo Matching in the Twentiescited by 23
  6. 2025ActiveGAMER: Active GAussian Mapping through Efficient Renderingcited by 19
  7. 2025StereoVLA: Enhancing Vision-Language-Action Models with Stereo Visioncited by 16
  8. 2023DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Diffusion Modelcited by 11
  9. 2024Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matchingcited by 11
  10. 2025Unleashing the Temporal Potential of Stereo Event Cameras for Continuous-Time 3D Object Detectioncited by 3
  11. 2024Visual SLAM with 3D Gaussian Primitives and Depth Priors Enabling Novel View Synthesiscited by 2
  12. 2024Unsupervised Stereo Matching Network For VHR Remote Sensing Images Based On Error Predictioncited by 1
11 more
  1. 2024Probabilistic Modeling of Disparity Uncertainty for Robust and Efficient Stereo Matchingcited by 1
  2. 2024Robust Confidence Intervals in Stereo Matching using Possibility Theorycited by 0
  3. 2024Rectified Iterative Disparity for Stereo Matchingcited by 0
  4. 2024Superpixel Cost Volume Excitation for Stereo Matchingcited by 0
  5. 2025CMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo Matchingcited by 0
  6. 2025Uncertainty Quantification Framework for Aerial and UAV Photogrammetry through Error Propagationcited by 0
  7. 2025Integrating Disparity Confidence Estimation into Relative Depth Prior-Guided Unsupervised Stereo Matchingcited by 0
  8. 2026Two-Stream Interactive Joint Learning of Scene Parsing and Geometric Vision Taskscited by 0
  9. 2026StereoAdapter-2: Globally Structure-Consistent Underwater Stereo Depth Estimationcited by 0
  10. 2026Revisiting Matching Response and Swept Feature Volumes for Wide-baseline Omnidirectional Stereocited by 0
  11. 2026A Calibration Audit of Confidence in Feed-Forward 3D Reconstruction Modelscited by 0