Sparse-to-Dense Hint Guided Stereo-LiDAR Fusion

One challenge in stereo-LiDAR fusion arises from the sparsity and non-uniform distribution of LiDAR data. Existing methods expand sparse LiDAR data to produce semi-dense hints as guidance for fusion. However, the absence of depth cues beyond the expanded areas may still limit performance. To address this challenge, we propose a novel sparse-to-dense hint guided stereo-LiDAR fusion method. The key idea is to use a dense hint map generated by a lightweight network as guidance, with sparse LiDAR points and a monocular image as inputs. The dense hints are then employed to construct and explicitly regularize a multi-modal cost volume via integrating the geometric cues from the hints and the visual information from the images to produce better stereo prediction. The construction and aggregation of cost volume follow a well-designed coarse-to-fine strategy along with a pixel-wise search range adjustment module, facilitating fast computation while preserving fine details. Finally, a confidence-based fusion module is performed to adaptively produce the ultimate prediction based on the monocular and stereo estimations. The experimental results show that our method significantly outperforms existing methods with high inference efficiency across multiple benchmark datasets. To contribute to the community, we will release the code at: https://github.com/LiAngLA66/DG-Fusion.