mlsanityData to 14 Oct 2026

Depth Estimation by Combining Binocular Stereo and Monocular Structured-Light

arXiv 2203.10493 ↗First posted 20 Mar 2022cs.CVcited by 4

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Abstract (arXiv)

It is well known that the passive stereo system cannot adapt well to weak texture objects, e.g., white walls. However, these weak texture targets are very common in indoor environments. In this paper, we present a novel stereo system, which consists of two cameras (an RGB camera and an IR camera) and an IR speckle projector. The RGB camera is used both for depth estimation and texture acquisition. The IR camera and the speckle projector can form a monocular structured-light (MSL) subsystem, while the two cameras can form a binocular stereo subsystem. The depth map generated by the MSL subsystem can provide external guidance for the stereo matching networks, which can improve the matching accuracy significantly. In order to verify the effectiveness of the proposed system, we build a prototype and collect a test dataset in indoor scenes. The evaluation results show that the Bad 2.0 error of the proposed system is 28.2% of the passive stereo system when the network RAFT is used. The dataset and trained models are available at https://github.com/YuhuaXu/MonoStereoFusion.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 30 of its 51 references that are in the map. First posted on arXiv 20 Mar 2022.

Builds on

30 of 51 references.

References · 30

  1. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,415
  2. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 881
  3. 2018Pyramid Stereo Matching Networkcited by 489
  4. 2019Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matchingcited by 417
  5. 2017End-to-End Learning of Geometry and Context for Deep Stereo Regressioncited by 360
  6. 2019GA-Net: Guided Aggregation Net for End-to-end Stereo Matchingcited by 244
  7. 2017Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Ratescited by 199
  8. 2020Non-Local Spatial Propagation Network for Depth Completioncited by 195
  9. 2017Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Imagecited by 161
  10. 2021CFNet: Cascade and Fused Cost Volume for Robust Stereo Matchingcited by 154
  11. 2019Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awarenesscited by 151
  12. 2018DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Imagecited by 129
18 more
  1. 2020HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matchingcited by 120
  2. 2019DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatchcited by 114
  3. 2018Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Networkcited by 110
  4. 2019Depth Completion from Sparse LiDAR Data with Depth-Normal Constraintscited by 108
  5. 2018SegStereo: Exploiting Semantic Information for Disparity Estimationcited by 88
  6. 2018Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentationcited by 76
  7. 2014Computing the Stereo Matching Cost with a Convolutional Neural Networkcited by 74
  8. 2017Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matchingcited by 63
  9. 2019Learning Parallax Attention for Stereo Image Super-Resolutioncited by 56
  10. 2021Bilateral Grid Learning for Stereo Matching Networkscited by 46
  11. 2021Depth Completion with Twin Surface Extrapolation at Occlusion Boundariescited by 41
  12. 2019EdgeStereo: An Effective Multi-Task Learning Network for Stereo Matching and Edge Detectioncited by 39
  13. 2020Parallax Attention for Unsupervised Stereo Correspondence Learningcited by 39
  14. 2018ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systemscited by 35
  15. 2021A Decomposition Model for Stereo Matchingcited by 25
  16. 2019Guided Stereo Matchingcited by 24
  17. 2015Kinect Range Sensing: Structured-Light versus Time-of-Flight Kinectcited by 16
  18. 2019IRS: A Large Naturalistic Indoor Robotics Stereo Dataset to Train Deep Models for Disparity and Surface Normal Estimationcited by 13

Built on by

Cited by year (2026 to 14 Oct)

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

4 papers.

  1. 2024A Survey on Deep Stereo Matching in the Twentiescited by 23
  2. 2023Drone-NeRF: Efficient NeRF Based 3D Scene Reconstruction for Large-Scale Drone Surveycited by 11
  3. 2025Robust Single-shot Structured Light 3D Imaging via Neural Feature Decodingcited by 2
  4. 2026NSL-SLAM: High-Fidelity Neural Structured-Light Depth for Practical SLAM and Reconstructioncited by 0