DAWN: Accelerating Point Cloud Object Detection via Object-Aware Partitioning and 3D Similarity-Based Filtering

As a fundamental perception task, 3D point cloud detection has become essential for applications in autonomous driving and robotics. However, point cloud detection faces significant challenges of high computational cost due to complex point processing operations. To address this issue, we propose DAWN, an acceleration framework for point cloud object detection that identifies partial similarities between adjacent frames and reduces computational cost by filtering redundant points. DAWN uses object-aware partitioning that defines boundaries based on previous detection results for localized similarity analysis. Additionally, it applies axis-sorted point selection to refine partitioning for point clouds with non-uniform distribution. An efficient 3D similarity algorithm then filters redundant points to reduce computational load. DAWN enables flexible latencyaccuracy trade-offs by tuning point filtering ratios. Experimental results show that DAWN achieves a $1.59 \times$ average speedup and up to $1.70 \times$ on state-of-the-art detection networks by filtering more than $50 \%$ of points on average, with negligible impact on accuracy.