# Spectral Consistency-Guided Multiview Point Cloud Registration for Low-Overlap Scenes

arXiv: https://arxiv.org/abs/2609.12417 · HTML: https://mlsanity.com/paper/2609.12417/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 47 references that are in the map.
- First posted on arXiv 11 Sep 2026.

## Builds on (most cited first, 24 of 24)

- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions](https://mlsanity.com/paper/1603.08182/) (2016)
- [PREDATOR: Registration of 3D Point Clouds with Low Overlap](https://mlsanity.com/paper/2011.13005/) (2020)
- [PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency](https://mlsanity.com/paper/2103.05465/) (2021)
- [3D Registration with Maximal Cliques](https://mlsanity.com/paper/2305.10854/) (2023)
- [Learning multiview 3D point cloud registration](https://mlsanity.com/paper/2001.05119/) (2020)
- [You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors](https://mlsanity.com/paper/2109.00182/) (2021)
- [SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration](https://mlsanity.com/paper/2203.14453/) (2022)
- [Learning Transformation Synchronization](https://mlsanity.com/paper/1901.09458/) (2019)
- [HARA: A Hierarchical Approach for Robust Rotation Averaging](https://mlsanity.com/paper/2111.08831/) (2021)
- [DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds](https://mlsanity.com/paper/1811.11397/) (2018)
- [Joint Alignment of Multiple Point Sets with Batch and Incremental Expectation-Maximization](https://mlsanity.com/paper/1609.01466/) (2016)
- [Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting](https://mlsanity.com/paper/2304.00467/) (2023)
- [DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization](https://mlsanity.com/paper/2212.06331/) (2022)
- [Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching](https://mlsanity.com/paper/2512.01850/) (2025)
- [On Bundle Adjustment for Multiview PointCloud Registration](https://mlsanity.com/paper/2108.02976/) (2021)
- [A New Framework for Registration of Semantic Point Clouds from Stereo and RGB-D Cameras](https://mlsanity.com/paper/2012.03683/) (2020)
- [Nonparametric Continuous Sensor Registration](https://mlsanity.com/paper/2001.04286/) (2020)
- [Deep Learning-Based Point Cloud Registration: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2404.13830/) (2024)
- [Robust Multi-view Registration of Point Sets with Laplacian Mixture Model](https://mlsanity.com/paper/2110.13744/) (2021)
- [RKHS-BA: A Robust Correspondence-Free Multi-View Registration Framework with Semantic Point Clouds](https://mlsanity.com/paper/2403.01254/) (2024)
- [Pairwise Point Cloud Registration using Graph Matching and Rotation-invariant Features](https://mlsanity.com/paper/2105.02151/) (2021)
- [Multiview Point Cloud Registration via Optimization in an Autoencoder Latent Space](https://mlsanity.com/paper/2504.21467/) (2025)
- [Correspondence-Free Multiview Point Cloud Registration via Depth-Guided Joint Optimisation](https://mlsanity.com/paper/2506.18922/) (2025)

## Abstract (arXiv)

Multiview point cloud registration is particularly challenging in low-overlap scenes, where reliable correspondences are limited and incorrect pairwise transformations can affect global pose estimation. In addition, registering all scan pairs is computationally expensive because many pairs provide weak geometric information. To address these problems, we propose GMPCR, a non-learning-based spectral consistency-guided framework for efficient and robust multiview point cloud registration. GMPCR builds a refined second-order compatibility structure from initial correspondences and uses its dominant spectral response to evaluate both correspondence reliability and scan-pair confidence. This allows unreliable correspondences to be filtered and informative scan pairs to be selected before relative transformation estimation, leading to a sparse pose graph and reduced pairwise registration cost. For each retained scan pair, maximal-clique-based hypothesis generation is used to estimate reliable relative transformations. The resulting pose graph is further refined by an adaptive history-aware synchronization scheme, in which the effect of residual history is adjusted according to changes in the global rotation residual. A recovery mechanism also allows down-weighted edges to regain confidence when their global consistency improves. Experiments on 3DMatch, 3DLoMatch, ScanNet, and ETH demonstrate the effectiveness of GMPCR. It achieves registration recalls of 97.2% and 89.6% on 3DMatch and 3DLoMatch, respectively, while maintaining competitive performance on ScanNet and ETH. The results show that GMPCR provides a favorable balance among registration accuracy, robustness to low overlap, and computational efficiency. The code is publicly available at https://github.com/swccj/gmpcr.