ColabSfM: Collaborative Structure-from-Motion by Point Cloud Registration
Johan Edstedt, André Mateus, Alberto Jaenal
Abstract
Figure 1 . Our proposed registration paradigm for collaborative SfM reconstructions (ColabSfM). Given two input SfM reconstructions P, Q of the same scene, the task is to estimate the relative similarity transform (s, R, t) between them. Our first contribution is to address this as a point cloud registration problem, using only 3D SfM tracks. For this, we do not rely on the visual descriptors, but on the 3D coordinates of the points P, Q, their normals N, M and, optionally, but not necessarily, features X, Y. To make point cloud registration methods perform well on this task, we propose as our second contribution a scalable pipeline to construct synthetic training datasets for SfM registration. Finally, we propose an improved version of RoITr [64] as registration method f θ .
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 1c836881-86ef-4957-bc60-a16a7f6f2dccCited by top-tier papers2
- CRFT: Consistent-Recurrent Feature Flow Transformer for Cross-Modal Image RegistrationXuecong Liu, Mengzhu Ding, Zixuan Sun, Zhang Li et al.CVPR 2026 · 4 citations
- Towards Visual Localization Interoperability: Cross-Feature for Collaborative Visual Localization and MappingAlberto Jaenal, Paula Carbó Cubero, José Araujo, André MateusICCV 2025 · 1 citation
Builds on18
- Deep Closest Point: Learning Representations for Point Cloud RegistrationYue Wang, Justin SolomonICCV 2019 · 1,026 citations
- Fully Convolutional Geometric FeaturesChristopher B. Choy, Jaesik Park, Vladlen KoltunICCV 2019 · 807 citations
- DISK: Learning local features with policy gradientMichal J. Tyszkiewicz, Pascal Fua, Eduard TrullsNeurIPS 2020 · 652 citations
- Geometric Transformer for Fast and Robust Point Cloud RegistrationZheng Qin, Hao Yu, Changjian Wang, Yulan Guo et al.CVPR 2022 · 436 citations
- Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly- ThroughsHaithem Turki, Deva Ramanan, Mahadev SatyanarayananCVPR 2022 · 364 citations
Related papers
- SIRA-PCR: Sim-to-Real Adaptation for 3D Point Cloud RegistrationSuyi Chen, Hao Xu, Ru Li, Guanghui Liu et al.ICCV 2023 · 30 citations
- Learning Instance-Aware Correspondences for Robust Multi-Instance Point Cloud Registration in Cluttered ScenesZhiyuan Yu, Zheng Qin, Lintao Zheng, Kai XuCVPR 2024 · 13 citations
- DeepI2P: Image-to-Point Cloud Registration via Deep ClassificationJiaxin Li, Gim Hee LeeCVPR 2021
- Deep Hough Voting for Robust Global RegistrationJunha Lee, Seungwook Kim, Minsu Cho, Jaesik ParkICCV 2021 · 130 citations
- DReg-NeRF: Deep Registration for Neural Radiance FieldsYu Chen, Gim Hee LeeICCV 2023 · 24 citations
