Associate-3Ddet：三维点云目标检测的感知-概念关联
Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection
摘要：
从3D点云中检测目标检测仍然是一项具有挑战性的任务，尽管最近的研究利用深度学习技术突破了极限。由于点云数据存在严重的空间遮挡和点密度随距离变化的固有差异，同一物体在点云数据中的外观变化很大。因此，针对这种外观变化设计鲁棒的特征表示是3D目标检测方法中的关键问题。在本文中，我们创新性地提出了一种类似领域自适应的方法来增强特征表示的鲁棒性。更具体地说，我们弥合了感知域之间的差距，在感知域中的特征来自真实场景，在概念域中的特征是从由非遮挡点云丰富的详细信息组成的增强场景中提取的。这种领域自适应方法模拟了人脑在进行物体感知时的功能。大量的实验表明，我们简单而有效的方法从根本上提高了三维点云目标检测的性能，并达到了最先进的结果。
英文摘要：
Object detection from 3D point clouds remains a challenging task, though recent studies pushed the envelope with the deep learning techniques. Owing to the severe spatial occlusion and inherent variance of point density with the distance to sensors, appearance of a same object varies a lot in point cloud data. Designing robust feature representation against such appearance changes is hence the key issue in a 3D object detection method. In this paper, we innovatively propose a domain adaptation like approach to enhance the robustness of the feature representation. More specifically, we bridge the gap between the perceptual domain where the feature comes from a real scene and the conceptual domain where the feature is extracted from an augmented scene consisting of non-occlusion point cloud rich of detailed information. This domain adaptation approach mimics the functionality of the human brain when proceeding object perception. Extensive experiments demonstrate that our simple yet effective approach fundamentally boosts the performance of 3D point cloud object detection and achieves the state-of-the-art results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
