Minimal Solvers for 3D Scan Alignment With Pairs of Intersecting Lines
André Mateus, Srikumar Ramalingam, Pedro Miraldo
Abstract
We explore the possibility of using line intersection constraints for 3D scan registration. Typical 3D registration algorithms exploit point and plane correspondences, while line intersection constraints have not been used in the context of 3D scan registration before. Constraints from a match of pairs of intersecting lines in two 3D scans can be seen as two 3D line intersections, a plane correspondence, and a point correspondence. In this paper, we present minimal solvers that combine these different type of constraints: 1) three line intersections and one point match; 2) one line intersection and two point matches; 3) three line intersections and one plane match; 4) one line intersection and two plane matches; and 5) one line intersection, one point match, and one plane match. To use all the available solvers, we present a hybrid RANSAC loop. We propose a non-linear refinement technique using all the inliers obtained from the RANSAC. Vast experiments with simulated data and two real-data data-sets show that the use of these features and the combined solvers improve the accuracy. The code is available at https://github.com/ 3DVisionISR/3DMinRegLineIntersect.
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 c36a57db-57db-4bf9-bb1f-e9f5c71627bcCited by top-tier papers3
- BANSAC: A dynamic BAyesian Network for adaptive SAmple ConsensusValter Piedade, Pedro MiraldoICCV 2023 · 16 citations
- 3DRegNet: A Deep Neural Network for 3D Point RegistrationGonçalo Dias Pais, Srikumar Ramalingam, Venu Madhav Govindu, Jacinto C. Nascimento et al.CVPR 2020
- PLMP - Point-Line Minimal Problems for Projective SfMKim Kiehn, Albin Ahlbäck, Kathlén KohnICCV 2025
Builds on3
- Deep Closest Point: Learning Representations for Point Cloud RegistrationYue Wang, Justin SolomonICCV 2019 · 1,026 citations
- DeepVCP: An End-to-End Deep Neural Network for Point Cloud RegistrationWeixin Lu, Guowei Wan, Yao Zhou, Xiangyu Fu et al.ICCV 2019 · 313 citations
- Efficient and Robust Registration on the 3D Special Euclidean GroupUttaran Bhattacharya, Venu Madhav GovinduICCV 2019 · 21 citations
Related papers
- Real-time Vanishing Point Detector Integrating Under-parameterized RANSAC and Hough TransformJianping Wu, Liang Zhang, Ye Liu, Ke ChenICCV 2021 · 17 citations
- Relative Pose Estimation for Multi-Camera Systems from Point Correspondences with Scale RatioBanglei Guan, Ji ZhaoACM MM 2022 · 7 citations
- Learning to Solve Hard Minimal ProblemsPetr Hruby, Timothy Duff, Anton Leykin, Tomás PajdlaCVPR 2022
- PluckerNet: Learn To Register 3D Line ReconstructionsLiu Liu, Hongdong Li, Haodong Yao, Ruyi ZhaCVPR 2021
- DualReg: Dual-Space Filtering and Reinforcement for Rigid RegistrationJiayi Li, Yuxin Yao, Qiuhang Lu, Juyong ZhangCVPR 2026
