提出统一点云存储格式与高效处理管道，显著提升大规模点云数据读取速度。
Joint Optimization of Storage and Loading for High-Performance 3D Point Cloud Data Processing
- 设计.PcRecord统一格式，减少存储占用并加速数据加载
- 多阶段并行流水线使处理速度最高提升25.4倍（Ascend上SUN RGB-D）
- 适合自动驾驶、机器人感知等需快速处理点云的场景
随着计算机视觉与深度学习的快速发展，3D视觉在自动驾驶、机器人感知和增强现实等领域取得显著进展。作为3D信息的重要表示形式，点云数据规模庞大且结构复杂，传统算法难以高效处理大规模数据集。不同存储格式（如PLY、XYZ、BIN）增加了数据处理的复杂性，尽管使用BIN和NPY等二进制格式可提升访问速度，但数据加载与处理仍耗时严重。为此，本文提出.PcRecord统一存储格式，结合高性能数据处理流水线，采用多阶段并行架构优化计算资源利用，显著提升处理效率。实验表明，在GPU上平均提速6.61倍（ModelNet40）、2.69倍（S3DIS）、2.23倍（ShapeNet）、3.09倍（Kitti）、8.07倍（SUN RGB-D）、5.67倍（ScanNet）；在Ascend芯片上分别达6.9倍、1.88倍、1.29倍、2.28倍、25.4倍、19.3倍。
原文摘要 · Abstract (English)
With the rapid development of computer vision and deep learning, significant advancements have been made in 3D vision, partic- ularly in autonomous driving, robotic perception, and augmented reality. 3D point cloud data, as a crucial representation of 3D information, has gained widespread attention. However, the vast scale and complexity of point cloud data present significant chal- lenges for loading and processing and traditional algorithms struggle to handle large-scale datasets.The diversity of storage formats for point cloud datasets (e.g., PLY, XYZ, BIN) adds complexity to data handling and results in inefficiencies in data preparation. Al- though binary formats like BIN and NPY have been used to speed up data access, they still do not fully address the time-consuming data loading and processing phase. To overcome these challenges, we propose the .PcRecord format, a unified data storage solution designed to reduce the storage occupation and accelerate the processing of point cloud data. We also introduce a high-performance data processing pipeline equipped with multiple modules. By leveraging a multi-stage parallel pipeline architecture, our system optimizes the use of computational resources, significantly improving processing speed and efficiency. This paper details the im- plementation of this system and demonstrates its effectiveness in addressing the challenges of handling large-scale point cloud datasets.On average, our system achieves performance improvements of 6.61x (ModelNet40), 2.69x (S3DIS), 2.23x (ShapeNet), 3.09x (Kitti), 8.07x (SUN RGB-D), and 5.67x (ScanNet) with GPU and 6.9x, 1.88x, 1.29x, 2.28x, 25.4x, and 19.3x with Ascend.
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