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 DA...

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Bibliographic Details
Published in:2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors: Tang, Dongdong, Mao, Yu, Wang, Weilan, Guan, Nan, Kuo, Tei-Wei, Xue, Chun Jason
Format: Conference Proceeding
Language:English
Published: IEEE 22.06.2025
Subjects:
Accuracy
Computational efficiency
Filters
Object detection
Object recognition
Partitioning algorithms
Point cloud compression
Robots
Three-dimensional displays
Tuning
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Summary: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.
DOI:10.1109/DAC63849.2025.11132746