SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics
This work addresses a problem for autonomous driving by providing more precise 3D object detection, but it appears incremental as it builds on existing methods.
The paper tackles the limitation of existing LiDAR-only 3D object detectors by enhancing semantic information to capture relationships between data units, resulting in performance improvements on the KITTI benchmark.
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