Identifying moving objects is a crucial capability for autonomous navigation, consistent map generation, and future trajectory prediction of objects. In this paper, we propose a novel network that addresses the challenge of segmenting moving objects in 3D LiDAR scans. Our approach not only predicts point-wise moving labels but also detects instance information of main traffic participants. Such a design helps determine which instances are actually moving and which ones are temporarily static in the current scene. Our method exploits a sequence of point clouds as input and quantifies them into 4D voxels. We use 4D sparse convolutions to extract motion features from the 4D voxels and inject them into the current scan. Then, we extract spatio-temporal features from the current scan for instance detection and feature fusion. Finally, we design an upsample fusion module to output point-wise labels by fusing the spatio-temporal features and predicted instance information. We evaluated our approach on the LiDAR-MOS benchmark based on SemanticKITTI and achieved better moving object segmentation performance compared to state-of-the-art methods, demonstrating the effectiveness of our approach in integrating instance information for moving object segmentation. Furthermore, our method shows superior performance on the Apollo dataset with a pre-trained model on SemanticKITTI, indicating that our method generalizes well in different scenes.The code and pre-trained models of our method will be released at https://github.com/nubot-nudt/InsMOS.
翻译:定位移动对象是自主导航、一致的地图生成和未来天体轨迹预测的关键能力。 在本文中, 我们提出一个新的网络, 以应对在 3D LiDAR 扫描中分割移动对象的挑战 。 我们的方法不仅预测了点动标签, 而且还检测了主要交通参与者的示例信息 。 这样的设计有助于确定当前场景中哪些事件实际上在移动,哪些是暂时静止的。 我们的方法将点云序列用作输入, 并将其量化为 4D voxels 。 我们使用 4D 稀疏的云流来从 4D voxels 中提取运动功能, 并将它们输入到当前扫描中 。 然后, 我们从当前扫描中提取 spotio- 时空特性特性特性特性来进行分解 。 最后, 我们设计了一个向输出点点标签的增缩模块模块模块模块模块模块和图像信息。 我们的模型/ 系统前系统化方法将展示我们工具的运行效率 。</s>