Efficient L 0 resampling of point sets

Abstract(#br)The point data captured by laser scanners or consumer depth cameras are often contaminated with severe noises and outliers. In this paper, we propose a resampling method in an L 0 minimization framework to process such low quality data. Our framework can produce a set of clean, uniformly distributed, geometry-maintaining and feature-preserving oriented points. The L 0 norm improves the robustness to noises (outliers) and the ability to keep sharp features, but introduces a significant efficiency degradation. To further improve the efficiency of our L 0 point set resampling, we propose two accelerating algorithms including optimization-based local half-sampling and interleaved regularization. As demonstrated by the experimental results, the accelerated method is about an order of magnitude faster than the original, while achieves state-of-the-art point set consolidation performance

IterativePFN: True Iterative Point Cloud Filtering

The quality of point clouds is often limited by noise introduced during their capture process. Consequently, a fundamental 3D vision task is the removal of noise, known as point cloud filtering or denoising. State-of-the-art learning based methods focus on training neural networks to infer filtered displacements and directly shift noisy points onto the underlying clean surfaces. In high noise conditions, they iterate the filtering process. However, this iterative filtering is only done at test time and is less effective at ensuring points converge quickly onto the clean surfaces. We propose IterativePFN (iterative point cloud filtering network), which consists of multiple IterationModules that model the true iterative filtering process internally, within a single network. We train our IterativePFN network using a novel loss function that utilizes an adaptive ground truth target at each iteration to capture the relationship between intermediate filtering results during training. This ensures that the filtered results converge faster to the clean surfaces. Our method is able to obtain better performance compared to state-of-the-art methods. The source code can be found at: https://github.com/ddsediri/IterativePFN.Comment: This paper has been accepted to the IEEE/CVF CVPR Conference, 202