mlsanityData to 14 Oct 2026

Noise-Aware Unsupervised Deep Lidar-Stereo Fusion

arXiv 1904.03868 ↗First posted 8 Apr 2019cs.CVcited by 31

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Abstract (arXiv)

In this paper, we present LidarStereoNet, the first unsupervised Lidar-stereo fusion network, which can be trained in an end-to-end manner without the need of ground truth depth maps. By introducing a novel "Feedback Loop'' to connect the network input with output, LidarStereoNet could tackle both noisy Lidar points and misalignment between sensors that have been ignored in existing Lidar-stereo fusion studies. Besides, we propose to incorporate a piecewise planar model into network learning to further constrain depths to conform to the underlying 3D geometry. Extensive quantitative and qualitative evaluations on both real and synthetic datasets demonstrate the superiority of our method, which outperforms state-of-the-art stereo matching, depth completion and Lidar-Stereo fusion approaches significantly.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Apr 2019.

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Citing papers per year: 2021: 5; 2022: 11; 2023: 4; 2024: 8; 2025: 3.

31 papers.

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  1. 2022Multi-modal Sensor Fusion for Auto Driving Perception: A Surveycited by 76
  2. 2021Positive Sample Propagation along the Audio-Visual Event Linecited by 69
  3. 2022Implicit Motion Handling for Video Camouflaged Object Detectioncited by 60
  4. 2022Contrastive Positive Sample Propagation along the Audio-Visual Event Linecited by 50
  5. 2024Multimodal Fusion on Low-quality Data: A Comprehensive Surveycited by 49
  6. 2022HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detectioncited by 31
  7. 2022Vicinity Vision Transformercited by 22
  8. 2023Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain Generalizationcited by 18
  9. 2024SAM-PM: Enhancing Video Camouflaged Object Detection using Spatio-Temporal Attentioncited by 11
  10. 2024Locality-aware Cross-modal Correspondence Learning for Dense Audio-Visual Events Localizationcited by 11
  11. 2021S3: Learnable Sparse Signal Superdensity for Guided Depth Estimationcited by 10
  12. 2022Unsupervised confidence for LiDAR depth maps and applicationscited by 10
19 more
  1. 2022Deep Laparoscopic Stereo Matching with Transformerscited by 9
  2. 2022Mind The Edge: Refining Depth Edges in Sparsely-Supervised Monocular Depth Estimationcited by 8
  3. 2023Active Stereo Without Pattern Projectorcited by 8
  4. 2021LiDARTouch: Monocular metric depth estimation with a few-beam LiDARcited by 7
  5. 2024LiDAR-Event Stereo Fusion with Hallucinationscited by 7
  6. 2022CU-Net: LiDAR Depth-Only Completion With Coupled U-Netcited by 6
  7. 2022Multi-View Guided Multi-View Stereocited by 5
  8. 2024Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth Conversioncited by 4
  9. 2021Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimationand 3D Object Detectioncited by 3
  10. 2021Fast and Accurate: Video Enhancement using Sparse Depthcited by 2
  11. 2023Shape-Net: Room Layout Estimation from Panoramic Images Robust to Occlusion using Knowledge Distillation with 3D Shapes as Additional Inputscited by 2
  12. 2024Camouflaged Object Tracking: A Benchmarkcited by 2
  13. 2025Audio Does Matter: Importance-Aware Multi-Granularity Fusion for Video Moment Retrievalcited by 2
  14. 2022Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matchingcited by 1
  15. 2023A survey on deep learning approaches for data integration in autonomous driving systemcited by 1
  16. 2024Active Stereo in the Wild through Virtual Pattern Projectioncited by 0
  17. 2024Multi-QuAD: Multi-Level Quality-Adaptive Dynamic Network for Reliable Multimodal Classificationcited by 0
  18. 2025A Reinforcement Learning Method for Environments with Stochastic Variables: Post-Decision Proximal Policy Optimization with Dual Critic Networkscited by 0
  19. 2025Preserving Guidance in Cost-Volume Retrieval under Extreme LiDAR Sparsity in Iterative Stereocited by 0