SAMBLE: Shape-Specific Point Cloud Sampling for an Optimal Trade-Off Between Local Detail and Global Uniformity

2025-04-28

AI Summary

Point cloud sampling faces two key challenges: (1) existing learning-based methods often produce unrecognizable sampling patterns and overemphasize sharp edges at the expense of global uniformity; (2) they ignore intrinsic point distribution variations across shapes, adopting a one-size-fits-all strategy. This paper proposes the first shape-adaptive learnable sampling framework. It models local semantic importance via a sparse attention map and explicitly encodes shape-specific semantics using binned shape encoding. Integrated with an end-to-end differentiable sampling network, the framework is jointly optimized with downstream tasks. Evaluated on benchmarks including ModelNet40 under the Few-Point setting, our method achieves average performance gains of 3.2% in classification, segmentation, and reconstruction—significantly surpassing the generalization limitations of fixed sampling strategies.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Learning with Manifolds
• Computer Vision: Diffusion Models for Vision

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Balancing local detail and global uniformity in point cloud sampling
• Addressing biased sampling from over-focusing on edge details
• Learning shape-specific sampling strategies for varied point distributions

Innovation

• Sparse Attention Map for shape-specific sampling
• Bin-based Learning to balance local and global details
• Improved performance in few-point sampling scenarios