Point-ITR: Task-Oriented Importance Sampling for Large-Scale 3D Point Clouds in Manufacturing

Abstract

The increasing adoption of advanced three-dimensional (3D) scanning technologies has made large-scale point clouds containing millions of 3D measurement points standard in applications like manufacturing. However, processing immense amounts of 3D data imposes significant computational loads, often resulting in discarded critical information and suboptimal outcomes for downstream tasks. This paper introduces Point-ITR, a task-oriented sampling method tailored for regression tasks, which selectively retains the most informative points within large-scale point clouds. Specifically, we propose a gradient-based importance sampling framework for intra-sample selection (selecting points within a 3D point cloud) and a feature-based weighting scheme for inter-sample selection (selecting among different 3D point cloud sub-samples). Additionally, we introduce an iterative random sampling (ItrRS) module for preprocessing and an Offset Residual Block that utilizes a reference design model to learn structural features and accelerate both training and testing, which allows a simple fully connected network to process large-scale point clouds. Our approach improves prediction accuracy across downstream tasks while ensuring that the rich details captured are fully utilized for interpretation, offering a more effective and efficient solution. We validate our methodology through simulation studies and real-world case applications in additive manufacturing, demonstrating its robustness and practical applicability. © 2004-2012 IEEE.