论文标题
3D对象在点云上使用局部接地感和场景表面的自适应表示
3D Object Detection on Point Clouds using Local Ground-aware and Adaptive Representation of scenes' surface
论文作者
论文摘要
提出了一种新颖的，自适应的基础感知和具有成本效益的3D对象检测管道。与其Uni-Planar对应物相比（使用单个平面对整个3D场景建模的方法）相比，本文引入的地面表示形式更加准确，而速度更快。地面表示的新颖性在于场景的地面在激光雷达感知问题中表示，以及以（成本效益的）方式计算的方式。此外，提出的对象检测管道通过整合动态推理场景表面的能力，最终在两阶段的liDAR对象检测管道中实现了新的最新3D对象检测性能，从而建立在传统的两阶段对象检测模型上。
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.
