An Overview Of 3D Object Detection
This addresses the challenge of detecting sparse objects like pedestrians and cyclists in autonomous driving systems, but appears incremental as it combines existing methods.
The paper tackles 3D object detection in LiDAR point clouds by proposing a framework that fuses RGB and point cloud data, using 2D detection models to localize regions of interest and lift them to 3D space, evaluated on the nuScenes dataset.
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.