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Accelerating Point Cloud Sampling by Parallel Structure Deconstruction
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DOI：10.1109/TPDS.2025.3622691.png)
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Abstract
En 中文
Point cloud processing is fundamental to applications such as autonomous driving, robotic navigation, and 3D reconstruction. Sampling is a crucial process in point cloud processing, but current sampling algorithms perform poorly when deployed on edge devices due to limited computational resources. This stems from three main reasons. First, the robustness of sampling is weak; even after denoising, residual noise remains, and existing edge sampling methods struggle to ignore noisy sampling nodes. Second, there’s a lack of structural sampling, popular algorithms like FPS tend to cover global information without considering special structures for focused sampling. Third, system efficiency is poor, traditional Farthest Point Sampling (FPS) and k-Nearest Neighbor Search (kNN) have terrible time and space complexities of $O(n^{2})$ with low parallelism, limiting system throughput, especially for large-scale point cloud processing. To bridge this gap, we propose a novel point cloud sampling algorithm based on parallel graph deconstruction named DPS (Deconstruction-based Point Cloud Sampling) and its multi-scale version DPS_MS with better denoising effect. First, during graph construction, we employ multi-scale graphing and add a preprocessing stage to filter out some noise nodes, reducing the noise rate. Second, in the sampling stage, we categorize important structures into edge nodes and dense nodes based on the reverse neighbor count of graph nodes, assigning them higher sampling weights to supplement structural information. Finally, we use a highly parallel locality-sensitive hashing algorithm to accelerate neighbor search and reduce memory consumption, we achieved data-level parallelization through the comprehensive parallelization of our algorithm. Through rigorous qualitative and quantitative validation on classification and segmentation tasks, we demonstrate that DPS, while maintaining accuracy, increases point cloud sampling speed by 22.08 times compared to the FPS algorithm, the implementation achieved a 75.1-fold improvement in system throughput and a maximum parallel speedup of 10.32x, and improves the Accuracy/FLOPs (M) ratio to 8.68.
Keywords:
Point cloud
kNN
FPS
parallel
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