针对点云上采样中的噪声与不均匀问题，提出渐进式局部表面估计方法。
PLATYPUS: Progressive Local Surface Estimator for Arbitrary-Scale Point Cloud Upsampling
- 基于曲率的采样策略，优先聚焦高曲率区域以捕捉复杂结构。
- 首次在点云数据上实现课程学习，按曲率分布自动评估样本难易。
- 显著提升点云密度与均匀性，尤其在复杂曲面处表现优异。
3D点云在自动驾驶和机器人等领域日益重要，但传感器获取的原始数据常存在噪声和稀疏问题，影响下游任务。因此，点云上采样对提升密度与均匀性至关重要。现有方法通过将随机查询点投影到稀疏点云的潜在表面来实现，但常产生异常值、分布不均，并难以处理高曲率及复杂结构区域。本文提出渐进式局部表面估计器（PLSE），通过基于曲率的采样策略，有效捕获复杂区域的局部特征，优先选择高曲率区域进行采样。同时引入课程学习策略，利用点云内部的曲率分布自然评估样本难度，首次实现点云数据上的课程学习。实验表明，该方法显著优于现有方法，在准确性和细节还原方面均取得更好效果，生成高质量、密集且均匀的点云。
原文摘要 · Abstract (English)
3D point clouds are increasingly vital for applications like autonomous driving and robotics, yet the raw data captured by sensors often suffer from noise and sparsity, creating challenges for downstream tasks. Consequently, point cloud upsampling becomes essential for improving density and uniformity, with recent approaches showing promise by projecting randomly generated query points onto the underlying surface of sparse point clouds. However, these methods often result in outliers, non-uniformity, and difficulties in handling regions with high curvature and intricate structures. In this work, we address these challenges by introducing the Progressive Local Surface Estimator (PLSE), which more effectively captures local features in complex regions through a curvature-based sampling technique that selectively targets high-curvature areas. Additionally, we incorporate a curriculum learning strategy that leverages the curvature distribution within the point cloud to naturally assess the sample difficulty, enabling curriculum learning on point cloud data for the first time. The experimental results demonstrate that our approach significantly outperforms existing methods, achieving high-quality, dense point clouds with superior accuracy and detail.
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