针对点云采样中局部细节与全局均匀性的矛盾，提出形状自适应采样方法。
SAMBLE: Shape-Specific Point Cloud Sampling for an Optimal Trade-Off Between Local Detail and Global Uniformity
- 基于稀疏注意力图与分箱学习，为不同形状定制采样策略。
- 在少点采样下仍能兼顾边缘细节与整体分布均匀性。
- 适用于点云分类、分割等下游任务，尤其适合形状多样的数据。
随着多个领域对3D数据精确高效表示的需求增长，点云采样已成为3D计算机视觉的关键研究方向。近年来，学习型采样方法因其可与下游任务联合训练而受到关注，但现有方法或生成难以识别的采样模式，或过度聚焦锐利边缘导致采样偏差。此外，它们普遍忽略不同形状间点分布的自然差异，对所有点云采用统一采样策略。本文提出稀疏注意力图与分箱学习方法（SAMBLE），旨在学习形状特异的点云采样策略。SAMBLE有效平衡了边缘点采样以保留局部细节与全局分布均匀性之间的关系，在多个常见点云下游任务中表现更优，即使在少点采样场景下亦具备良好性能。
原文摘要 · Abstract (English)
Driven by the increasing demand for accurate and efficient representation of 3D data in various domains, point cloud sampling has emerged as a pivotal research topic in 3D computer vision. Recently, learning-to-sample methods have garnered growing interest from the community, particularly for their ability to be jointly trained with downstream tasks. However, previous learning-based sampling methods either lead to unrecognizable sampling patterns by generating a new point cloud or biased sampled results by focusing excessively on sharp edge details. Moreover, they all overlook the natural variations in point distribution across different shapes, applying a similar sampling strategy to all point clouds. In this paper, we propose a Sparse Attention Map and Bin-based Learning method (termed SAMBLE) to learn shape-specific sampling strategies for point cloud shapes. SAMBLE effectively achieves an improved balance between sampling edge points for local details and preserving uniformity in the global shape, resulting in superior performance across multiple common point cloud downstream tasks, even in scenarios with few-point sampling.
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