This work leverages the continuous sweeping motion of LiDAR scanning to concentrate object detection efforts on specific regions that receive a change in point data from one frame to another. We achieve this by using a sliding time window with short strides and consider the temporal dimension by storing convolution results between passes. This allows us to ignore unchanged regions, significantly reducing the number of convolution operations per forward pass without sacrificing accuracy. This data reuse scheme introduces extreme sparsity to detection data. To exploit this sparsity, we extend our previous work on scatter-based convolutions to allow for data reuse, and as such propose Sparse Scatter-Based Convolution Algorithm with Temporal Data Recycling (SSCATeR). This operation treats incoming LiDAR data as a continuous stream and acts only on the changing parts of the point cloud. By doing so, we achieve the same results with as much as a 6.61-fold reduction in processing time. Our test results show that the feature maps output by our method are identical to those produced by traditional sparse convolution techniques, whilst greatly increasing the computational efficiency of the network.

翻译：本研究利用激光雷达扫描的连续旋转运动，将目标检测的计算集中在点云数据发生帧间变化的特定区域上。我们通过采用短步长的滑动时间窗口来实现这一目标，并通过在连续扫描之间存储卷积结果来考虑时间维度。这使得我们能够忽略未发生变化的区域，从而在不牺牲精度的前提下，显著减少每次前向传播所需的卷积运算量。这种数据重用方案为检测数据引入了极高的稀疏性。为了充分利用这种稀疏性，我们将先前基于散射的卷积工作扩展至支持数据重用，并由此提出了具有时间数据循环利用的稀疏散射卷积算法（SSCATeR）。该操作将输入的激光雷达数据视为连续流，并仅作用于点云中发生变化的部分。通过这种方式，我们在获得相同结果的同时，处理时间最多可减少至原来的6.61倍。测试结果表明，本方法输出的特征图与传统稀疏卷积技术生成的特征图完全相同，同时大幅提升了网络的计算效率。