论文标题
基于点云的果蝇启发的3D移动对象检测
Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds
论文作者
论文摘要
3D移动对象检测是动态场景分析中最关键的任务之一。在本文中，我们提出了一种新型果蝇启发的3D运动对象检测方法，使用LiDAR传感器。根据基本运动检测器的理论，我们基于果蝇的浅视觉神经途径开发了一个运动探测器。该检测器对物体的运动敏感，并且可以很好地抑制背景噪声。通过不同的连接模式设计神经回路，该方法以粗略的方式搜索运动区域，并提取每个运动区域的点云以形成移动的对象建议。然后使用改进的3D对象检测网络来估计每个建议的点云，并有效地生成3D边界框和对象类别。我们在广泛使用的Kitti基准上评估了所提出的方法，并通过对运动检测任务使用拟议的方法获得了最先进的性能。
3D moving object detection is one of the most critical tasks in dynamic scene analysis. In this paper, we propose a novel Drosophila-inspired 3D moving object detection method using Lidar sensors. According to the theory of elementary motion detector, we have developed a motion detector based on the shallow visual neural pathway of Drosophila. This detector is sensitive to the movement of objects and can well suppress background noise. Designing neural circuits with different connection modes, the approach searches for motion areas in a coarse-to-fine fashion and extracts point clouds of each motion area to form moving object proposals. An improved 3D object detection network is then used to estimate the point clouds of each proposal and efficiently generates the 3D bounding boxes and the object categories. We evaluate the proposed approach on the widely-used KITTI benchmark, and state-of-the-art performance was obtained by using the proposed approach on the task of motion detection.
