融合三维高斯点染和分数蒸馏采样的三维点云补全方法

刘立程; 刘柏菁; 潘丹; 曾安; 杨宝瑶; 赵靖亮

doi:10.12052/gdutxb.240133

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融合三维高斯点染和分数蒸馏采样的三维点云补全方法

计算机科学与技术 | 更新时间：2026-05-27

- 融合三维高斯点染和分数蒸馏采样的三维点云补全方法
- 3D Point Cloud Completion Method Integrating 3D Gaussian Splatting and Score Distillation Sampling
- 广东工业大学学报 2025年42卷第4期页码：20-28
- 作者机构：
  
  1. 广东工业大学信息工程学院,广东,广州,510006
  2. 广东技术师范大学电子与信息学院,广东,广州,510665
  3. 广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  刘立程(1972–) ，男，副教授，博士，主要研究方向为计算机视觉、图像处理，E-mail：celcliu@gdut.edu.cn
  潘丹(1975–) ，男，教授，高级工程师，博士，主要研究方向为人工智能、计算机视觉、医学图像处理等，E-mail：pandan@gpnu.edu.cn
- 基金信息：
  
  广东省重点领域研发计划项目(2021B0101220006);国家自然科学基金资助项目(61976058，62102098);广东省科技计划项目(2019A050510041);广东省自然科学基金资助项目(2021A1515012300);广东省基础与应用基础研究区域联合基金资助项目(2022A1515140096);广州市科技计划项目(202103000034，202201010266)
- DOI：10.12052/gdutxb.240133
  中图分类号： TP183
- 纸质出版：2025
- 稿件说明：
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刘立程, 刘柏菁, 潘丹, 等. 融合三维高斯点染和分数蒸馏采样的三维点云补全方法[J]. 广东工业大学学报, 2025, 42(4): 20–28.

Liu Licheng, Liu Baijing, Pan Dan, et al. 3D Point Cloud Completion Method Integrating 3D Gaussian Splatting and Score Distillation Sampling[J]. 2025, 42(4): 20-28.
刘立程, 刘柏菁, 潘丹, 等. 融合三维高斯点染和分数蒸馏采样的三维点云补全方法[J]. 广东工业大学学报, 2025, 42(4): 20–28. DOI： 10.12052/gdutxb.240133

Liu Licheng, Liu Baijing, Pan Dan, et al. 3D Point Cloud Completion Method Integrating 3D Gaussian Splatting and Score Distillation Sampling[J]. 2025, 42(4): 20-28. DOI： 10.12052/gdutxb.240133.

摘要

针对现有三维点云补全方法依赖于配对数据的有监督学习，导致数据成本高昂且泛化能力不足的问题，本文提出了一种无需配对数据进行训练的三维点云补全方法。结合三维高斯点染技术和分数蒸馏采样技术，将不完整点云转换为三维高斯模型，并通过预训练的二维扩散模型引导三维高斯模型逐步优化，预测完整点云，实现三维点云补全任务。引入点密集化操作以增加输入点云的稠密度，增强初始三维高斯的质量，并采用渐进式相机采样策略在优化初期控制相机采样范围，提升了优化效率。实验结果表明，本文方法在RedWood-3DScan数据集上取得了优于现有方法的性能。消融实验进一步验证了所提出优化方案的有效性，证明了本方法在处理真实数据时的优越性。

Abstract

This study addressed the limitations of existing 3D point cloud completion methods

which rely on paired data and supervised learning

resulting in high data costs and limited generalization capabilities. This paper proposed a novel 3D point cloud completion method without requiring paired data for training by integrating 3D Gaussian splatting and Score Distillation Sampling (SDS) techniques. In the proposed method

incomplete point clouds were transformed into 3D Gaussian models

which were iteratively optimized using a pre-trained 2D diffusion model to predict complete point clouds and fulfill the completion task. To enhance the initial 3D Gaussian models

the authors introduced a point densification technique that increased the density of input point clouds. Additionally

a progressive camera sampling strategy was adopted during the early optimization stages to control the camera sampling range

thereby improving optimization efficiency. The experimental results demonstrate that the proposed method outperforms existing approaches on the RedWood-3DScan dataset. Ablation studies further validate the effectiveness of the optimization strategies

confirming the superiority of the proposed method in handling real-world data.

关键词

Keywords

references

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