3D Object Detection on Point Clouds using Local Ground-aware and Adaptive Representation of scenes' surface
This work addresses the problem of accurate and efficient 3D object detection for autonomous driving or robotics, but it is incremental as it builds on traditional two-stage models.
The paper tackles 3D object detection on point clouds by proposing a novel ground-aware and adaptive surface representation, which is ~10x faster and more accurate than uni-planar methods, and achieves state-of-the-art performance in two-stage Lidar object detection pipelines.
A novel, adaptive ground-aware, and cost-effective 3D Object Detection pipeline is proposed. The ground surface representation introduced in this paper, in comparison to its uni-planar counterparts (methods that model the surface of a whole 3D scene using single plane), is far more accurate while being ~10x faster. The novelty of the ground representation lies both in the way in which the ground surface of the scene is represented in Lidar perception problems, as well as in the (cost-efficient) way in which it is computed. Furthermore, the proposed object detection pipeline builds on the traditional two-stage object detection models by incorporating the ability to dynamically reason the surface of the scene, ultimately achieving a new state-of-the-art 3D object detection performance among the two-stage Lidar Object Detection pipelines.