3D Semantic Segmentation-Driven Representations for 3D Object Detection

In autonomous driving, 3D detection provides more precise information to downstream tasks, including path planning and motion estimation, compared to 2D detection. Therefore, the need for 3D detection research has emerged. However, although single and multi-view images and depth maps obtained from the camera were used, detection accuracy was relatively low compared to other modality-based detectors due to the lack of geometric information. The proposed multi-modal 3D object detection combines semantic features obtained from images and geometric features obtained from point clouds, but there are difficulties in defining unified representation to fuse data existing in different domains and synchronization between them. In this paper, we propose SeSame : point-wise semantic feature as a new presentation to ensure sufficient semantic information of the existing LiDAR-only based 3D detection. Experiments show that our approach outperforms previous state-of-the-art at different levels of difficulty in car and performance improvement on the KITTI object detection benchmark. Our code is available at https://github.com/HAMA-DL-dev/SeSame