  In autonomous driving, 3D object detection provides more precise information
for downstream tasks, including path planning and motion estimation, compared
to 2D object detection. In this paper, we propose SeSame: a method aimed at
enhancing semantic information in existing LiDAR-only based 3D object
detection. This addresses the limitation of existing 3D detectors, which
primarily focus on object presence and classification, thus lacking in
capturing relationships between elemental units that constitute the data, akin
to semantic segmentation. Experiments demonstrate the effectiveness of our
method with performance improvements on the KITTI object detection benchmark.
Our code is available at https://github.com/HAMA-DL-dev/SeSame
