# Noise-Aware Unsupervised Deep Lidar-Stereo Fusion

arXiv: https://arxiv.org/abs/1904.03868 · HTML: https://mlsanity.com/paper/1904.03868/

## Key facts

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

## Built on by (most cited first, 30 of 31)

- [Multi-modal Sensor Fusion for Auto Driving Perception: A Survey](https://mlsanity.com/paper/2202.02703/) (2022)
- [Positive Sample Propagation along the Audio-Visual Event Line](https://mlsanity.com/paper/2104.00239/) (2021)
- [Implicit Motion Handling for Video Camouflaged Object Detection](https://mlsanity.com/paper/2203.07363/) (2022)
- [Contrastive Positive Sample Propagation along the Audio-Visual Event Line](https://mlsanity.com/paper/2211.09980/) (2022)
- [Multimodal Fusion on Low-quality Data: A Comprehensive Survey](https://mlsanity.com/paper/2404.18947/) (2024)
- [HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection](https://mlsanity.com/paper/2206.15157/) (2022)
- [Vicinity Vision Transformer](https://mlsanity.com/paper/2206.10552/) (2022)
- [Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain Generalization](https://mlsanity.com/paper/2312.09254/) (2023)
- [SAM-PM: Enhancing Video Camouflaged Object Detection using Spatio-Temporal Attention](https://mlsanity.com/paper/2406.05802/) (2024)
- [Locality-aware Cross-modal Correspondence Learning for Dense Audio-Visual Events Localization](https://mlsanity.com/paper/2409.07967/) (2024)
- [S3: Learnable Sparse Signal Superdensity for Guided Depth Estimation](https://mlsanity.com/paper/2103.02396/) (2021)
- [Unsupervised confidence for LiDAR depth maps and applications](https://mlsanity.com/paper/2210.03118/) (2022)
- [Deep Laparoscopic Stereo Matching with Transformers](https://mlsanity.com/paper/2207.12152/) (2022)
- [Mind The Edge: Refining Depth Edges in Sparsely-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2212.05315/) (2022)
- [Active Stereo Without Pattern Projector](https://mlsanity.com/paper/2309.12315/) (2023)
- [LiDARTouch: Monocular metric depth estimation with a few-beam LiDAR](https://mlsanity.com/paper/2109.03569/) (2021)
- [LiDAR-Event Stereo Fusion with Hallucinations](https://mlsanity.com/paper/2408.04633/) (2024)
- [CU-Net: LiDAR Depth-Only Completion With Coupled U-Net](https://mlsanity.com/paper/2210.14898/) (2022)
- [Multi-View Guided Multi-View Stereo](https://mlsanity.com/paper/2210.11467/) (2022)
- [Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth Conversion](https://mlsanity.com/paper/2404.07545/) (2024)
- [Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimationand 3D Object Detection](https://mlsanity.com/paper/2103.03977/) (2021)
- [Fast and Accurate: Video Enhancement using Sparse Depth](https://mlsanity.com/paper/2103.08764/) (2021)
- [Shape-Net: Room Layout Estimation from Panoramic Images Robust to Occlusion using Knowledge Distillation with 3D Shapes as Additional Inputs](https://mlsanity.com/paper/2304.12624/) (2023)
- [Camouflaged Object Tracking: A Benchmark](https://mlsanity.com/paper/2408.13877/) (2024)
- [Audio Does Matter: Importance-Aware Multi-Granularity Fusion for Video Moment Retrieval](https://mlsanity.com/paper/2508.04273/) (2025)
- [Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching](https://mlsanity.com/paper/2210.01436/) (2022)
- [A survey on deep learning approaches for data integration in autonomous driving system](https://mlsanity.com/paper/2306.11740/) (2023)
- [Active Stereo in the Wild through Virtual Pattern Projection](https://mlsanity.com/paper/2406.04345/) (2024)
- [Multi-QuAD: Multi-Level Quality-Adaptive Dynamic Network for Reliable Multimodal Classification](https://mlsanity.com/paper/2412.14489/) (2024)
- [A Reinforcement Learning Method for Environments with Stochastic Variables: Post-Decision Proximal Policy Optimization with Dual Critic Networks](https://mlsanity.com/paper/2504.05150/) (2025)

## 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.