3D Semantic Segmentation-Driven Representations for 3D Object Detection
Hayeon O Kunsoo Huh
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