Introduction

Point cloud (PC) processing tasks—such as completion, upsampling, denoising, and colorization—are crucial in applications like autonomous driving and 3D reconstruction. Despite substantial advancements, prior approaches often address each of these tasks independently, with separate models focused on individual issues. However, this isolated approach fails to account for the fact that defects like incompleteness, low resolution, noise, and lack of color frequently coexist, with each defect influencing and correlating with the others. Simply applying these models sequentially can lead to error accumulation from each model, along with increased computational costs. To address these challenges, we introduce SuperPC, the first unified diffusion model capable of concurrently handling all four tasks. Our approach employs a three-level-conditioned diffusion framework, enhanced by a novel spatial-mix-fusion strategy, to leverage the correlations among these four defects for simultaneous, efficient processing. We show that SuperPC outperforms the state-of-the-art specialized models as well as their combination on all four individual tasks.

We propose SuperPC, a novel neural architecture that jointly solves inherent shortcomings in the raw point clouds, including noise, sparsity, incompleteness, and the absence of color. To the best of our knowledge, it is the first single diffusion model that can simultaneously tackle the four major challenges in the field of point cloud processing. Red points denote high noise for visualization.

Method Overview

The architecture of the SuperPC model shown above integrates input images and point clouds to establish three-level conditions through innovative raw, local, and global modules. These conditions are seamlessly integrated into each step of the diffusion process, enabling SuperPC to utilize all levels of information from the two input modalities.

Visualization Results

1. Detailed results on the poinc cloud completion, upsampling, and denoising tasks

The qualitative results on the point cloud (a) completion, (b) upsampling, and (c) denoising tasks. For each subfigure, from left to right are the results for ShapeNet, TartanAir, and KITTI-360.

2. More visualization results

Publication

1. SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization.

   Yi Du, Zhipeng Zhao, Shaoshu Su, Sharath Golluri, Haoze Zheng, Runmao Yao, Chen Wang.

   IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
   BibTex Cite

   @inproceedings{du2025superpc,
     title = {{SuperPC}: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization},
     author = {Du, Yi and Zhao, Zhipeng and Su, Shaoshu and Golluri, Sharath and Zheng, Haoze and Yao, Runmao and Wang, Chen},
     booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
     year = {2025}
   }
   

   Du, Yi and Zhao, Zhipeng and Su, Shaoshu and Golluri, Sharath and Zheng, Haoze and Yao, Runmao and Wang, Chen, "SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.