论文标题
基于LIDAR的3D移动对象分割的有效空间信息融合
Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation
论文作者
论文摘要
准确的移动对象细分是自动驾驶的必不可少的任务。它可以为许多下游任务（例如避免碰撞，路径计划和静态地图构建）提供有效的信息。如何有效利用空间信息是3D激光雷达移动对象分割（LIDAR-MOS）的关键问题。在这项工作中，我们提出了一个新型的深神经网络，利用了时空信息和LIDAR扫描的不同表示方式，以提高LIDAR-MOS性能。具体而言，我们首先使用基于图像图像的双分支结构来分别处理可以从连续的LiDAR扫描获得的空间和时间信息，然后使用运动引导的注意模块组合它们。我们还通过3D稀疏卷积使用点完善模块来融合LIDAR范围图像和点云表示的信息，并减少对象边界上的工件。我们验证了我们提出的方法对Semantickitti的LiDAR-MOS基准的有效性。我们的方法在LIDAR-MOS IOU方面大大优于最先进的方法。从设计的粗到精细体系结构中受益，我们的方法以传感器框架速率在线运行。我们方法的实现可作为开源可用：https：//github.com/haomo-ai/motionseg3d。
Accurate moving object segmentation is an essential task for autonomous driving. It can provide effective information for many downstream tasks, such as collision avoidance, path planning, and static map construction. How to effectively exploit the spatial-temporal information is a critical question for 3D LiDAR moving object segmentation (LiDAR-MOS). In this work, we propose a novel deep neural network exploiting both spatial-temporal information and different representation modalities of LiDAR scans to improve LiDAR-MOS performance. Specifically, we first use a range image-based dual-branch structure to separately deal with spatial and temporal information that can be obtained from sequential LiDAR scans, and later combine them using motion-guided attention modules. We also use a point refinement module via 3D sparse convolution to fuse the information from both LiDAR range image and point cloud representations and reduce the artifacts on the borders of the objects. We verify the effectiveness of our proposed approach on the LiDAR-MOS benchmark of SemanticKITTI. Our method outperforms the state-of-the-art methods significantly in terms of LiDAR-MOS IoU. Benefiting from the devised coarse-to-fine architecture, our method operates online at sensor frame rate. The implementation of our method is available as open source at: https://github.com/haomo-ai/MotionSeg3D.
