LiDAR-based semantic perception tasks are critical yet challenging for autonomous driving. Due to the motion of objects and static/dynamic occlusion, temporal information plays an essential role in reinforcing perception by enhancing and completing single-frame knowledge. Previous approaches either directly stack historical frames to the current frame or build a 4D spatio-temporal neighborhood using KNN, which duplicates computation and hinders realtime performance. Based on our observation that stacking all the historical points would damage performance due to a large amount of redundant and misleading information, we propose the Sparse Voxel-Adjacent Query Network (SVQNet) for 4D LiDAR semantic segmentation. To take full advantage of the historical frames high-efficiently, we shunt the historical points into two groups with reference to the current points. One is the Voxel-Adjacent Neighborhood carrying local enhancing knowledge. The other is the Historical Context completing the global knowledge. Then we propose new modules to select and extract the instructive features from the two groups. Our SVQNet achieves state-of-the-art performance in LiDAR semantic segmentation of the SemanticKITTI benchmark and the nuScenes dataset.

基于我们的观察，将所有的历史点堆叠会损害性能，因为有大量多余和误导性信息，我们提出了用于4D激光雷达语义分割的稀疏体素相邻查询网络（SVQNet），通过将历史点分成两组来充分利用历史帧的高效性，一组是携带局部增强知识的体素相邻邻域，另一组是补充全局知识的历史情境，然后我们提出了新的模块来从这两组中选择和提取指导性特征，我们的SVQNet在SemanticKITTI基准和nuScenes数据集的激光雷达语义分割中实现了最先进的性能。

SVQNet：用于4D时空LiDAR语义分割的稀疏体素邻近查询网络