The human brain can effortlessly recognize and localize objects, whereas current 3D object detection methods based on LiDAR point clouds still report inferior performance for detecting occluded and distant objects: the point cloud appearance varies greatly due to occlusion, and has inherent variance in point densities along the distance to sensors. Therefore, designing feature representations robust to such point clouds is critical. Inspired by human associative recognition, we propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We bridge the gap between the perceptual domain, where features are derived from real scenes with sub-optimal representations, and the conceptual domain, where features are extracted from augmented scenes that consist of non-occlusion objects with rich detailed information. A feasible method is investigated to construct conceptual scenes without external datasets. We further introduce an attention-based re-weighting module that adaptively strengthens the feature adaptation of more informative regions. The network's feature enhancement ability is exploited without introducing extra cost during inference, which is plug-and-play in various 3D detection frameworks. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed. Experiments on nuScenes and Waymo datasets also validate the versatility of our method.
翻译:人类大脑可以不遗余力地识别和定位天体,而目前基于LIDAR点云的3D天体探测方法仍然报告在探测隐蔽和遥远天体方面的性能低劣:点云的外观因隐蔽而大不相同,在感应器远处的点密度存在固有的差异。因此,设计对点云具有强力特征的特征显示非常关键。在人类关联性认识的启发下,我们提议了一个新型的3D天体探测框架,将物体的完整特性通过域内适应加以结合。我们缩小了概念域与概念领域之间的差距,即从有亚最佳表现的真实场景中产生特征,而概念领域则从由具有丰富详细信息的非隐蔽物体组成的扩大场景中提取特征。我们调查了一种可行的方法,在没有外部数据集的情况下构建概念场景。我们进一步引入了基于关注的重力调整模块,以适应性地加强更多信息区的特征适应。网络的增强能力在推断过程中没有引入额外的成本,即各种3D探测框架中的插件和动作。我们在3D级的精确度探测方法上实现了新的状态的实验性标准。