基于原始点云的三维目标检测算法.pdf

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计算机工程与码:A 中图分类号:TP391 doi：.1002--0239
3D Object Detection Algorithm Based on Raw Point Clouds
ZHANG Dongdong, GUO Jie, CHEN Yang
Army Engineering University of PLA, Nanjing 210007, China
Abstract：Aiming at the existing problems in 3d object detection, such as difficult data sampling, insufficient feature
extraction, limited receptive field and low regression quality of candidate bounding box, based on 3DSSD 3D object
detection algorithm, we present a single stage and anchor-free 3D object detection algorithm RPV-SSD(Random Point
Voxel Single Stage Object Detector)which based on the raw point clouds. The algorithm is composed of five parts,
namely, the random voxel sampling layer, the 3D sparse convolution layer, the feature aggregation layer, the candidate
point generation layer, and the region proposal network layer. By aggregating the point-wise feature of the keypoints,
the sparse convolution feature of voxel, and the BEV(bird eye view) feature, the category, 3D bounding box and
orientation of the object can be predicted. Experiments on KITTI datasets show that the algorithm performs well on
the can not only hit the target in the truth label and regress an accurate bounding box, but also infer the
category and complete shape of the object from its incomplete point clouds, and improve the performance of object
detection.
Key words：deep learning; raw point clouds; object detection; single stage; anchor free
目标检测，作为三维数据处理与分析的基础技术、 示方法，具有获取简单、易于存储、可视性强、结构
基础算法，是计算机视觉当前热门研究方向之一。目 描述精细等优点，而且能够方便地与深度图、体素等
前主流的 3D 数据表示方法主要有深度图、三角网格、 其它数据格式相互转换，已成为最基本的 3D 数据格
体素和点云。其中，点云是最简单的一种 3D 数据表 式。近年来，随着深度传感器和三维激