End-to-End 3D Object Detection Using LiDAR Point Cloud

Engineering notes

Key topics: autonomous driving, autonomous vehicle, bird's eye view, end-to-end, 3d object detection, object detection, lidar, point cloud, prediction. See the paper for implementation details and experimental results.

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

Significant progress has been made in the field of au-tonomous vehicles. Object detection and tracking are the primary tasks for any autonomous vehicle. The task of object detection in autonomous vehicles relies on a variety of sensors like cameras, and LiDAR. Although image features are typically preferred, numerous approaches take spatial data as input. Exploiting this information we present an approach wherein, using a novel encoding of the LiDAR point cloud we infer the location of different classes near the autonomous vehicles. This approach does not implement a bird's eye view approach, which is generally applied for this application and thus saves the extensive pre-processing required. After studying the numerous networks and approaches, we have implemented a novel model intending to inculcate their advantages and avoid their shortcomings. The output is predictions about the location and orientation of objects in the scene in the form of 3D bounding boxes and labels of scene objects.

Links and sources

• Official / arXiv page
• PDF from original source

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