基于八叉树CNN的点云联合上采样与清理
Joint Point Cloud Upsampling and Cleaning with Octree-based CNNs
AI总结：
本文提出一种基于八叉树3D U-Net的简单高效方法，在单个网络中联合完成点云上采样与清理，整体处理点云使推理速度提升至少47倍，并在多个基准上取得最先进性能。
摘要：
从稀疏或含噪数据中恢复密集且均匀分布的点云仍然是一个重大挑战。近年来，这些任务取得了很大进展，但通常以日益复杂的模块或复杂的网络架构为代价，导致推理时间长和资源消耗巨大。相反，我们拥抱简洁，提出了一种简单而高效的方法，用于联合上采样和清理点云。我们的方法利用现成的基于八叉树的3D U-Net（OUNet）并进行少量修改，使得上采样和清理任务能够在单个网络中完成。我们的网络直接整体处理每个输入点云，而不是像以往工作那样处理每个点云块，这显著简化了实现，并使推理速度至少提高47倍。大量实验表明，我们的方法在一系列基准上以巨大的效率优势取得了最先进的性能。我们希望我们的方法能够作为简单的基线，并启发研究者重新思考点云上采样和清理的方法设计。
英文摘要：
Recovering dense and uniformly distributed point clouds from sparse or noisy data remains a significant challenge. Recently, great progress has been made on these tasks, but usually at the cost of increasingly intricate modules or complicated network architectures, leading to long inference time and huge resource consumption. Instead, we embrace simplicity and present a simple yet efficient method for jointly upsampling and cleaning point clouds. Our method leverages an off-the-shelf octree-based 3D U-Net (OUNet) with minor modifications, enabling the upsampling and cleaning tasks within a single network. Our network directly processes each input point cloud as a whole instead of processing each point cloud patch as in previous works, which significantly eases the implementation and brings at least 47 times faster inference. Extensive experiments demonstrate that our method achieves state-of-the-art performances under huge efficiency advantages on a series of benchmarks. We expect our method to serve simple baselines and inspire researchers to rethink the method design on point cloud upsampling and cleaning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
