SSCATeR: Sparse Scatter-Based Convolution Algorithm with Temporal Data Recycling for Real-Time 3D Object Detection in LiDAR Point Clouds

TL;DR

SSCATeR is a novel sparse convolution algorithm that leverages temporal data recycling in LiDAR point clouds, significantly reducing computation while maintaining detection accuracy for real-time 3D object detection.

Contribution

It introduces a temporal data recycling scheme into sparse convolutions, enabling efficient processing of LiDAR streams by focusing on changing regions.

Findings

Achieves up to 6.61-fold reduction in processing time.

Maintains identical feature map outputs to traditional methods.

Effectively exploits data sparsity for real-time detection.

Abstract

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.