针对车载点云数据量大问题，提出动态筛选关键目标点的方法。
On-the-Fly Object-aware Representative Point Selection in Point Cloud
- 先检测物体存在，再按预算分配采样点，保留关键信息。
- 在KITTI和nuScenes上不同采样率下均优于现有方法。
- 不依赖特定模型，可适配多种下游任务，适合自动驾驶场景。
点云对物体建模及自动驾驶车辆（AV）的驾驶任务至关重要，但其生成的数据量巨大，带来存储、带宽和处理成本挑战。为此，我们提出一种点云下采样的代表性点选择框架，能在有效过滤无关背景点的同时保留关键物体信息。方法包含两步：(1) 物体存在检测，采用无监督密度峰值分类器与有监督朴素贝叶斯分类器应对多样场景；(2) 采样预算分配，提出策略优先选取与物体相关的点并保持高信息保留率。在KITTI和nuScenes数据集上的大量实验表明，该方法在不同采样率下均持续优于当前最优基线，在效率与效果上表现更优。作为模型无关方案，本方法可无缝集成到多种下游模型中，是自动驾驶3D点云下采样工具链中一项有价值的可扩展补充。
原文摘要 · Abstract (English)
Point clouds are essential for object modeling and play a critical role in assisting driving tasks for autonomous vehicles (AVs). However, the significant volume of data generated by AVs creates challenges for storage, bandwidth, and processing cost. To tackle these challenges, we propose a representative point selection framework for point cloud downsampling, which preserves critical object-related information while effectively filtering out irrelevant background points. Our method involves two steps: (1) Object Presence Detection, where we introduce an unsupervised density peak-based classifier and a supervised Naïve Bayes classifier to handle diverse scenarios, and (2) Sampling Budget Allocation, where we propose a strategy that selects object-relevant points while maintaining a high retention rate of object information. Extensive experiments on the KITTI and nuScenes datasets demonstrate that our method consistently outperforms state-of-the-art baselines in both efficiency and effectiveness across varying sampling rates. As a model-agnostic solution, our approach integrates seamlessly with diverse downstream models, making it a valuable and scalable addition to the 3D point cloud downsampling toolkit for AV applications.
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