论文标题
3D对象检测的概述
An Overview Of 3D Object Detection
论文作者
论文摘要
Point Cloud 3D对象检测最近受到了重大关注，并成为3D计算机视觉社区中的一个积极研究主题。但是，由于点云的复杂性，识别LiDAR（光检测和范围）中的3D对象仍然是一个挑战。诸如行人，骑自行车的人或交通锥之类的物体通常以相当稀疏的点表示，这使得仅使用点云使检测非常复杂。在此项目中，我们提出了一个使用RGB和Point Cloud数据执行多类对象识别的框架。我们使用现有的2D检测模型在RGB图像上定位感兴趣的区域（ROI），然后在点云中进行像素映射策略，最后将初始2D边界框提升到3D空间。我们使用最近发布的Nuscenes数据集---大规模数据集包含许多数据格式---培训和评估我们所提出的体系结构。
Point cloud 3D object detection has recently received major attention and becomes an active research topic in 3D computer vision community. However, recognizing 3D objects in LiDAR (Light Detection and Ranging) is still a challenge due to the complexity of point clouds. Objects such as pedestrians, cyclists, or traffic cones are usually represented by quite sparse points, which makes the detection quite complex using only point cloud. In this project, we propose a framework that uses both RGB and point cloud data to perform multiclass object recognition. We use existing 2D detection models to localize the region of interest (ROI) on the RGB image, followed by a pixel mapping strategy in the point cloud, and finally, lift the initial 2D bounding box to 3D space. We use the recently released nuScenes dataset---a large-scale dataset contains many data formats---to training and evaluate our proposed architecture.
