Revisiting Point Cloud Simplification: A Learnable Feature Preserving Approach
This addresses the need for efficient 3D model processing for applications in computer vision and graphics, though it is incremental as it builds on existing simplification techniques with a learning-based approach.
The paper tackles the problem of high computational costs in point cloud simplification by proposing a fast learnable method that selects and repositions salient points to minimize visual perception error, achieving generalization to out-of-distribution shapes with zero-shot capabilities.
The recent advances in 3D sensing technology have made possible the capture of point clouds in significantly high resolution. However, increased detail usually comes at the expense of high storage, as well as computational costs in terms of processing and visualization operations. Mesh and Point Cloud simplification methods aim to reduce the complexity of 3D models while retaining visual quality and relevant salient features. Traditional simplification techniques usually rely on solving a time-consuming optimization problem, hence they are impractical for large-scale datasets. In an attempt to alleviate this computational burden, we propose a fast point cloud simplification method by learning to sample salient points. The proposed method relies on a graph neural network architecture trained to select an arbitrary, user-defined, number of points from the input space and to re-arrange their positions so as to minimize the visual perception error. The approach is extensively evaluated on various datasets using several perceptual metrics. Importantly, our method is able to generalize to out-of-distribution shapes, hence demonstrating zero-shot capabilities.