提出DWCNet，边补全边去噪，提升点云在真实场景下的修复能力。
Denoising-While-Completing Network (DWCNet): Robust Point Cloud Completion Under Corruption
- 设计噪声管理模块，结合对比学习与自注意力机制抑制噪声。
- 在多种污染数据上性能超越现有方法，尤其在真实世界数据上提升显著。
- 构建新基准数据集CPCCD，揭示现有方法在复杂退化下的局限性。
点云补全对自动驾驶、增强现实和机器人等3D视觉任务至关重要，但真实环境中因噪声和遮挡导致获取干净完整的点云极具挑战。现有多数补全网络基于合成数据训练，在面对真实世界退化时表现不佳。本文针对受多重退化影响的严重污染部分点云，提出一种同时完成补全与去噪的方法。为评估鲁棒性，我们构建了新数据集CPCCD，揭示当前方法在多样退化下的局限性。在此基础上，提出DWCNet（Denoising-While-Completing Network），其核心是噪声管理模块（NMM），利用对比学习和自注意力机制有效抑制噪声并建模结构关系。DWCNet在清洁及污染的合成与真实数据集上均达领先水平。代码与数据集将公开于https://github.com/keneniwt/DWCNET-Robust-Point-Cloud-Completion-against-Corruptions。
原文摘要 · Abstract (English)
Point cloud completion is crucial for 3D computer vision tasks in autonomous driving, augmented reality, and robotics. However, obtaining clean and complete point clouds from real-world environments is challenging due to noise and occlusions. Consequently, most existing completion networks -- trained on synthetic data -- struggle with real-world degradations. In this work, we tackle the problem of completing and denoising highly corrupted partial point clouds affected by multiple simultaneous degradations. To benchmark robustness, we introduce the Corrupted Point Cloud Completion Dataset (CPCCD), which highlights the limitations of current methods under diverse corruptions. Building on these insights, we propose DWCNet (Denoising-While-Completing Network), a completion framework enhanced with a Noise Management Module (NMM) that leverages contrastive learning and self-attention to suppress noise and model structural relationships. DWCNet achieves state-of-the-art performance on both clean and corrupted, synthetic and real-world datasets. The dataset and code will be publicly available at https://github.com/keneniwt/DWCNET-Robust-Point-Cloud-Completion-against-Corruptions
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