# 3D Object Detection on Point Clouds using Local Ground-aware and Adaptive Representation of scenes' surface Source: [3D Object Detection on Point Clouds using Local Ground-aware and Adaptive Representation of scenes' surface]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:23:05.784Z Authors: Arun CS Kumar, Disha Ahuja, Ashwath Aithal Published: 2020-02-02T05:42:23.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()