# LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery

arXiv: https://arxiv.org/abs/1905.02744 · HTML: https://mlsanity.com/paper/1905.02744/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2019.

## Built on by (most cited first, 13 of 13)

- [A Survey on Deep Stereo Matching in the Twenties](https://mlsanity.com/paper/2407.07816/) (2024)
- [Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain Generalization](https://mlsanity.com/paper/2312.09254/) (2023)
- [Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation](https://mlsanity.com/paper/2103.12964/) (2021)
- [Active Stereo Without Pattern Projector](https://mlsanity.com/paper/2309.12315/) (2023)
- [LiDAR-Event Stereo Fusion with Hallucinations](https://mlsanity.com/paper/2408.04633/) (2024)
- [UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion](https://mlsanity.com/paper/2204.07791/) (2022)
- [Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth Conversion](https://mlsanity.com/paper/2404.07545/) (2024)
- [Sparse LiDAR Assisted Self-supervised Stereo Disparity Estimation](https://mlsanity.com/paper/2112.15355/) (2021)
- [Refinement of Monocular Depth Maps via Multi-View Differentiable Rendering](https://mlsanity.com/paper/2410.03861/) (2024)
- [Robust and accurate depth estimation by fusing LiDAR and Stereo](https://mlsanity.com/paper/2207.06139/) (2022)
- [Active Stereo in the Wild through Virtual Pattern Projection](https://mlsanity.com/paper/2406.04345/) (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)
- [Preserving Guidance in Cost-Volume Retrieval under Extreme LiDAR Sparsity in Iterative Stereo](https://mlsanity.com/paper/2507.19738/) (2025)

## Abstract (arXiv)

An accurate depth map of the environment is critical to the safe operation of autonomous robots and vehicles. Currently, either light detection and ranging (LIDAR) or stereo matching algorithms are used to acquire such depth information. However, a high-resolution LIDAR is expensive and produces sparse depth map at large range; stereo matching algorithms are able to generate denser depth maps but are typically less accurate than LIDAR at long range. This paper combines these approaches together to generate high-quality dense depth maps. Unlike previous approaches that are trained using ground-truth labels, the proposed model adopts a self-supervised training process. Experiments show that the proposed method is able to generate high-quality dense depth maps and performs robustly even with low-resolution inputs. This shows the potential to reduce the cost by using LIDARs with lower resolution in concert with stereo systems while maintaining high resolution.