Stereo RGB and Deeper LIDAR Based Network for 3D Object Detection
This addresses the problem of improving 3D object detection for autonomous driving systems, but it appears incremental as it builds on existing methods by integrating stereo images with point clouds.
The paper tackles 3D object detection in autonomous driving by proposing the SRDL framework that combines stereo RGB images and LIDAR point clouds to leverage both semantic and spatial information, achieving decent experimental results on the KITTI benchmark.
3D object detection has become an emerging task in autonomous driving scenarios. Previous works process 3D point clouds using either projection-based or voxel-based models. However, both approaches contain some drawbacks. The voxel-based methods lack semantic information, while the projection-based methods suffer from numerous spatial information loss when projected to different views. In this paper, we propose the Stereo RGB and Deeper LIDAR (SRDL) framework which can utilize semantic and spatial information simultaneously such that the performance of network for 3D object detection can be improved naturally. Specifically, the network generates candidate boxes from stereo pairs and combines different region-wise features using a deep fusion scheme. The stereo strategy offers more information for prediction compared with prior works. Then, several local and global feature extractors are stacked in the segmentation module to capture richer deep semantic geometric features from point clouds. After aligning the interior points with fused features, the proposed network refines the prediction in a more accurate manner and encodes the whole box in a novel compact method. The decent experimental results on the challenging KITTI detection benchmark demonstrate the effectiveness of utilizing both stereo images and point clouds for 3D object detection.