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 this https URL

在自动驾驶中，相较于 2D 检测，3D 检测能够提供更精确的信息用于路径规划和运动估计。然而，由于缺乏几何信息，单一和多视角图像以及来自相机的深度图在检测精度上相对较低。本文提出了 SeSame：一种基于点的语义特征的新表达方法，以确保基于 LiDAR 的 3D 检测具有充足的语义信息。实验证明，我们的方法在 KITTI 物体检测基准测试中在不同难度级别和车辆上优于以前的最先进方法。