mlsanityData to 14 Oct 2026

Sparse LiDAR Assisted Self-supervised Stereo Disparity Estimation

arXiv 2112.15355 ↗First posted 31 Dec 2021cs.CVcited by 1

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Abstract (arXiv)

Deep stereo matching has made significant progress in recent years. However, state-of-the-art methods are based on expensive 4D cost volume, which limits their use in real-world applications. To address this issue, 3D correlation maps and iterative disparity updates have been proposed. Regarding that in real-world platforms, such as self-driving cars and robots, the Lidar is usually installed. Thus we further introduce the sparse Lidar point into the iterative updates, which alleviates the burden of network updating the disparity from zero states. Furthermore, we propose training the network in a self-supervised way so that it can be trained on any captured data for better generalization ability. Experiments and comparisons show that the presented method is effective and achieves comparable results with related methods.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 26 of its 31 references that are in the map. First posted on arXiv 31 Dec 2021.

Builds on

26 of 31 references.

References · 26

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14 more
  1. 2019Learning Guided Convolutional Network for Depth Completioncited by 111
  2. 2017Occlusion Aware Unsupervised Learning of Optical Flowcited by 101
  3. 2019Sparse and noisy LiDAR completion with RGB guidance and uncertaintycited by 79
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  5. 2018Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentationcited by 76
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  8. 2021PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matchingcited by 28
  9. 2019Guided Stereo Matchingcited by 24
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  11. 2021Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimationcited by 15
  12. 2019LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagerycited by 13
  13. 2021Probabilistic Spatial Distribution Prior Based Attentional Keypoints Matching Networkcited by 8
  14. 2021FAMINet: Learning Real-time Semi-supervised Video Object Segmentation with Steepest Optimized Optical Flowcited by 3

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Preserving Guidance in Cost-Volume Retrieval under Extreme LiDAR Sparsity in Iterative Stereocited by 0