局所地面認識と適応的表面表現を用いた点群3D物体検出
3D Object Detection on Point Clouds using Local Ground-aware and Adaptive Representation of scenes' surface
LiDAR点群の地面を局所的に適応表現することで従来の単一平面モデルより高精度かつ約10倍高速に推定し、二段階3D物体検出の性能を向上させた研究。
著者: Arun CS Kumar, Disha Ahuja, Ashwath Aithal
分類: cs.CV, cs.LG, cs.RO, eess.IV
原文アブストラクト
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.