# C-GenReg：通过多视图一致的几何到图像生成与概率模态融合实现无需训练的3D点云配准

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-17 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmobsz50r09wbsl1yw6vyadey
- 原文链接：https://arxiv.org/abs/2604.16680

## AI 摘要

C-GenReg是一种无需训练的3D点云配准框架，利用世界基础模型将输入几何转换为多视图一致的RGB图像，借助视觉基础模型(VFM)在图像域提取密集对应关系，再通过深度图映射回3D空间。该方法采用"Match-then-Fuse"概率冷融合策略，将生成RGB分支与原始几何分支的对应后验进行融合，无需额外学习即可提供校准置信度。作为零样本即插即用方案，C-GenReg所有模块均无需微调，在室内3DMatch、ScanNet及室外Waymo基准测试中展现出卓越的跨域泛化能力，并首次在真实室外LiDAR数据上实现生成式配准。

## 正文

We introduce C-GenReg, a training-free framework for 3D point cloud registration that leverages the complementary strengths of world-scale generative priors and registration-oriented Vision Foundation Models (VFMs). Current learning-based 3D point cloud registration methods struggle to generalize across sensing modalities, sampling differences, and environments. Hence, C-GenReg augments the geometric point cloud registration branch by transferring the matching problem into an auxiliary image domain, where VFMs excel, using a World Foundation Model to synthesize multi-view-consistent RGB representations from the input geometry. This generative transfer, preserves spatial coherence across source and target views without any fine-tuning. From these generated views, a VFM pretrained for finding dense correspondences extracts matches. The resulting pixel correspondences are lifted back to 3D via the original depth maps. To further enhance robustness, we introduce a "Match-then-Fuse" probabilistic cold-fusion scheme that combines two independent correspondence posteriors, that of the generated-RGB branch with that of the raw geometric branch. This principled fusion preserves each modality inductive bias and provides calibrated confidence without any additional learning. C-GenReg is zero-shot and plug-and-play: all modules are pretrained and operate without fine-tuning. Extensive experiments on indoor (3DMatch, ScanNet) and outdoor (Waymo) benchmarks demonstrate strong zero-shot performance and superior cross-domain generalization. For the first time, we demonstrate a generative registration framework that operates successfully on real outdoor LiDAR data, where no imagery data is available.