Diff-Reg: Diffusion Model in Doubly Stochastic Matrix Space for Registration Problem

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00380048" target="_blank" >RIV/68407700:21230/25:00380048 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-73650-6_10" target="_blank" >https://doi.org/10.1007/978-3-031-73650-6_10</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-73650-6_10" target="_blank" >10.1007/978-3-031-73650-6_10</a>

Result language

angličtina

Original language name

Diff-Reg: Diffusion Model in Doubly Stochastic Matrix Space for Registration Problem

Original language description

Establishing reliable correspondences is essential for 3D and 2D-3D registration tasks. Existing methods commonly leverage geometric or semantic point features to generate potential correspondences. However, these features may face challenges such as large deformation, scale inconsistency, and ambiguous matching problems (e.g., symmetry). Additionally, many previous methods, which rely on single-pass prediction, may struggle with local minima in complex scenarios. To mitigate these challenges, we introduce a diffusion matching model for robust correspondence construction. Our approach treats correspondence estimation as a denoising diffusion process within the doubly stochastic matrix space, which gradually denoises (refines) a doubly stochastic matching matrix to the ground-truth one for high-quality correspondence estimation. It involves a forward diffusion process that gradually introduces Gaussian noise into the ground truth matching matrix and a reverse denoising process that iteratively refines the noisy one. In particular, we deploy a lightweight denoising strategy during the inference phase. Specifically, once points/image features are extracted and fixed, we utilize them to conduct multiple-pass denoising predictions in the reverse sampling process. Evaluation of our method on both 3D and 2D3D registration tasks confirms its effectiveness. The code is available at https://github.com/wuqianliang/Diff-Reg.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GM22-23183M" target="_blank" >GM22-23183M: New generation of camera geometry solvers</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Computer Vision – ECCV 2024, Part LXV

ISBN

978-3-031-73649-0

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

19

Pages from-to

160-178

Publisher name

Springer, Cham

Place of publication

—

Event location

Milano

Event date

Sep 29, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001403061600010