Joint Point Cloud Upsampling and Cleaning with Octree-based CNNs
This addresses the problem of slow and resource-intensive point cloud processing for researchers and practitioners, offering an incremental improvement in efficiency.
The paper tackles the challenge of recovering dense and uniformly distributed point clouds from sparse or noisy data by presenting a simple and efficient method for joint upsampling and cleaning, achieving state-of-the-art performance with at least 47 times faster inference.
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.