DAWN: Accelerating Point Cloud Object Detection via Object-Aware Partitioning and 3D Similarity-Based Filtering
Tang, Dongdong Mao, Yu Wang, Weilan Guan, Nan Kuo, Tei-Wei Xue, Chun Jason
Tang, Dongdong
Mao, Yu
Wang, Weilan
Guan, Nan
Kuo, Tei-Wei
Xue, Chun Jason
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Department
Computer Science
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Type
Conference proceeding
Date
2025
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Language
English
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Abstract
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× average speedup and up to 1.70× on state-of-the-art detection networks by filtering more than 50% of points on average, with negligible impact on accuracy.
Citation
D. Tang, Y. Mao, W. Wang, N. Guan, T. -W. Kuo and C. J. Xue, "DAWN: Accelerating Point Cloud Object Detection via Object-Aware Partitioning and 3D Similarity-Based Filtering," 2025 62nd ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, 2025, pp. 1-7, doi: 10.1109/DAC63849.2025.11132746.
Source
Proceedings of the ACM/IEEE Design Automation Conference (DAC)
Conference
2025 62nd ACM/IEEE Design Automation Conference (DAC)
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Source
2025 62nd ACM/IEEE Design Automation Conference (DAC)
Publisher
IEEE