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.

本研究解决了从稀疏或嘈杂数据中恢复密集且均匀分布的点云这一重大挑战。提出了一种简单高效的方法，通过对现有的八叉树3D U-Net进行小幅修改，实现了在单个网络中同时进行点云的上采样和清理，显著提高了推理速度和效率，并在多项基准测试中达到先进性能。这一方法旨在为点云处理设计提供新的视角与基础。 

基于八叉树的卷积神经网络共同进行点云上采样和清理