Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimation and 3D Object Detection

Abstract : The ability to accurately detect and localize objects is recognized as being the most important for the perception of selfdriving cars. From 2D to 3D object detection, the most difficult is to determine the distance from the ego-vehicle to objects. Expensive technology like LiDAR can provide a precise and accurate depth information, so most studies have tended to focus on this sensor showing a performance gap between LiDAR-based methods and camera-based methods. Although many authors have investigated how to fuse LiDAR with RGB cameras, as far as we know there are no studies to fuse LiDAR and stereo in a deep neural network for the 3D object detection task. This paper presents SLS-Fusion, a new approach to fuse data from 4-beam LiDAR and a stereo camera via a neural network for depth estimation to achieve better dense depth maps and thereby improves 3D object detection performance. Since 4-beam LiDAR is cheaper than the well-known 64-beam LiDAR, this approach is also classified as a low-cost sensorsbased method. Through evaluation on the KITTI benchmark, it is shown that the proposed method significantly improves depth estimation performance compared to a baseline method. Also when applying it to 3D object detection, a new state of the art on low-cost sensor based method is achieved.
Document type :
Preprints, Working Papers, ...
Complete list of metadata
Contributor : Alain Crouzil Connect in order to contact the contributor
Submitted on : Monday, May 24, 2021 - 7:04:14 PM
Last modification on : Wednesday, June 9, 2021 - 10:00:34 AM
Long-term archiving on: : Wednesday, August 25, 2021 - 6:12:26 PM


Files produced by the author(s)


  • HAL Id : hal-03233457, version 1


Nguyen-Anh-Minh Mai, Pierre Duthon, Louahdi Khoudour, Alain Crouzil, Sergio Velastin. Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimation and 3D Object Detection. 2021. ⟨hal-03233457⟩



Record views


Files downloads