Autonomous driving paper index
Monocular 3D Object Detection Based on Pseudo-LiDAR Point Cloud for Autonomous Vehicles
One-line summary
In this paper, we propose an approach to eliminate the performance degradation caused by deviation of depth estimation and realize 3D object detection based on pseudo-LiDAR point cloud.
Engineering notes
Comparative results on KITTI 3D benchmark illustrate that in contrast to other methods, our scheme can achieve more reliable performance on both object localization and shape estimation.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Pseudo-LiDAR point clouds are generated from monocular image. Compared with the point cloud from LiDAR, it can provide denser data. Due to the inaccuracy of depth estimation, there is still a performance gap between the pseudo-LiDAR point cloud and the LiDAR one. In this paper, we propose an approach to eliminate the performance degradation caused by deviation of depth estimation and realize 3D object detection based on pseudo-LiDAR point cloud. Comparative results on KITTI 3D benchmark illustrate that in contrast to other methods, our scheme can achieve more reliable performance on both object localization and shape estimation.
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