Diff-PCR: Diffusion-Based Correspondence Searching in Doubly Stochastic Matrix Space for Point Cloud Registration

[2401.00436] Diff-PCR: Diffusion-Based Correspondence Searching in Doubly Stochastic Matrix Space for Point Cloud Registration Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2401.00436 (cs) [Submitted on 31 Dec 2023 ( v1 ), last revised 9 Apr 2026 (this version, v6)] Title: Diff-PCR: Diffusion-Based Correspondence Searching in Doubly Stochastic Matrix Space for Point Cloud Registration Authors: Haihua Shi , Qianliang Wu View a PDF of the paper titled Diff-PCR: Diffusion-Based Correspondence Searching in Doubly Stochastic Matrix Space for Point Cloud Registration, by Haihua Shi and 1 other authors View PDF HTML (experimental) Abstract: Efficiently identifying accurate correspondences between point clouds is crucial for both rigid and non-rigid point cloud registration. Existing methods usually rely on geometric or semantic feature embeddings to establish correspondences and then estimate transformations or flow fields. Recently, several state-of-the-art methods have adopted RAFT-like iterative updates to refine solutions. However, these methods still have two major limitations. First, their iterative refinement mechanism lacks transparency, and the update trajectory is largely fixed once the refinement starts, which may lead to suboptimal solutions. Second, they overlook the importance of explicitly refining the correspondence matrix before solving for transformations or flow fields. Most existing approaches compute candidate correspondences in feature space and project the resulting matching matrix only once by using Sinkhorn or dual-softmax normalization. Such a one-shot projection can be far from the globally optimal solution, and these methods usually do not model the distribution of the target matching matrix. In this paper, we propose a novel framework that exploits a denoising diffusion model to predict a search gradient for the optimal matching matrix in doubly stochastic matrix space. Specifically, the diffusion model learns a denoising direction, and the reverse denoising process iteratively searches for improved solutions along this learned direction, which approximates the maximum-likelihood direction of the target matching matrix. To improve efficiency, we design a lightweight denoising module and adopt the accelerated sampling strategy of the Denoising Diffusion Implicit Model (DDIM)\cite{song2020denoising}. Experimental results on 3DMatch/3DLoMatch and 4DMatch/4DLoMatch demonstrate the effectiveness of the proposed framework. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2401.00436 [cs.CV] (or arXiv:2401.00436v6 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2401.00436 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Qianliang Wu [ view email ] [v1] Sun, 31 Dec 2023 09:24:28 UTC (7,845 KB) [v2] Wed, 3 Jan 2024 01:42:22 UTC (7,845 KB) [v3] Tue, 16 Jan 2024 04:17:56 UTC (7,846 KB) [v4] Wed, 17 Jan 2024 04:21:47 UTC (7,846 KB) [v5] Mon, 26 May 2025 14:37:25 UTC (7,846 KB) [v6] Thu, 9 Apr 2026 06:05:55 UTC (3,398 KB) Full-text links: Access Paper: View a PDF of the paper titled Diff-PCR: Diffusion-Based Correspondence Searching in Doubly Stochastic Matrix Space for Point Cloud Registration, by Haihua Shi and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2024-01 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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