The paper introduces GMPCR, a non‑learning spectral consistency‑guided framework for multiview point cloud registration in low‑overlap scenes. GMPCR refines initial correspondences into a second‑order compatibility structure, uses spectral analysis to filter unreliable matches and select informative scan pairs, and then applies maximal‑clique hypothesis generation for robust relative transformations. The resulting sparse pose graph is further refined with an adaptive history‑aware synchronization scheme, and a recovery mechanism allows previously down‑weighted edges to regain confidence, achieving high registration recalls on benchmark datasets while reducing computational cost.
By Tianyu Li, Yanghong Lin, Shudong Zhou, Kui Yang, Jingru Zhang, Li Fang, Wei Yao
arXiv:2610.34271v1 Announce Type: new
Abstract: We propose a novel method for robust point cloud registration with only one tunable parameter. Our method, dubbed TLP, uses truncated $L_p$ functions w...
By Seong Hun Lee, Patrick Vandewalle, Javier Civera
GRIP is a pose‑conditioned refinement framework that improves pixel‑to‑point matching for image‑to‑point‑cloud registration. It mitigates the mismatch between grid‑based image descriptors and unordered point cloud descriptors by softly rendering learned 3D point features onto the image grid using Gaussian feature splatting. The resulting rendered point‑derived feature map is fused with image features via a pixel‑aligned transformer, enabling visual semantic and geometric cues to interact in a shared 2D representation, which is then decoded and propagated to finer resolutions for dense correspondence estimation and final pose refinement. Experiments on RGB‑D Scenes V2 and 7 Scenes show state‑of‑the‑art inlier ratios and competitive registration recall, especially under stricter evaluation thresholds.
By Karim Slimani, Catherine Achard, Eric Marchand, Brahim Tamadazte
MemReg introduces a multi-frame outdoor LiDAR point cloud registration network that leverages hybrid memory buffers to capture temporal correlations in streaming sequences. The system uses an implicit memory feature buffer and an explicit memory pose buffer to store and update pose-related temporal features, while a dynamic history weighting module adaptively fuses current and historical information. Experiments on KITTI, nuScenes, and Apollo-Southbay datasets show MemReg surpasses previous pair-wise methods and multi-frame SLAM systems, and it also performs competitively on indoor datasets such as 3DMatch, 3DLoMatch, and ScanNet.
By Jiuming Liu, Mengmeng Liu, Guangming Wang, Lihao Liu, Michael Ying Yang, Yunpeng Zhang, Francesco Nex, Hao Cheng, Hesheng Wang
SAGE (Semantic-Augmented Geometric Experts) is a new point‑cloud registration method that incorporates frozen, pre‑trained semantic representations into the registration pipeline without extra sensors or fine‑tuning. It uses a Semantic‑Guided Mixture of Experts (SG‑MoE) to route data so that each expert specializes in specific semantic‑geometric patterns, thereby reducing mismatches that plague geometry‑only approaches. On the 3DLoMatch benchmark, SAGE achieves the highest registration recall and, with the same number of refinement iterations, also attains the best inlier ratio and recall on both 3DLoMatch and 3DMatch.
By Tomoyasu Shimada, Kazuhiko Murasaki, Shoichiro Takeda, Shogo Sato, Riku Inoue, Toshihiko Nishimura, Ryuichi Tanida
The paper presents a lightweight 6D pose tracking method that operates on relative pose constraints instead of point-based optimization, using uncertainty derived from geometric alignment to weight these constraints. By avoiding direct inclusion of noisy point measurements, the approach achieves fast and robust long-term tracking with computational costs far lower than reconstruction-based trackers. Experiments on four real-world benchmarks show comparable accuracy to state-of-the-art methods while maintaining real-time performance.
By Xiaojie Zhang, Tom Fischer, Viktor Larsson, Eddy Ilg
arXiv:2609.25375v1 Announce Type: cross
Abstract: Global point-cloud registration remains challenging when limited overlap, repetitive geometry, and sensor noise produce correspondence sets dominated...
By Abolfazl Babanazari, Carson Cramer, Tyler Summers, Carlos Nieto, Kaveh Fathian
HiP3R introduces a hierarchical pose graph approach for long‑horizon 3D reconstruction, addressing the trade‑off between chunk‑level and frame‑level optimization. It first builds a robust chunk‑level Sim(3) graph to set the global layout and scale, then fixes this layout while estimating a single SE(3) camera pose per frame to merge overlapping predictions into a unified trajectory. Experiments on multiple benchmarks show that HiP3R improves camera trajectories and 3D reconstruction quality for both chunk‑based and streaming backends without extra training.
By Zunnan Xu, Shangchen Zhou, Yihang Luo, Chen Change Loy
NovaSplat is a model‑free pipeline for 6D object pose estimation that builds a 3D Gaussian Splatting (3DGS) model from a short smartphone video during onboarding. The 3DGS model serves both as a template for detection and coarse pose estimation and as a differentiable backbone for pose refinement, which optimizes pose using photometric and geometric losses under a continuous rotation parametrization. The method outperforms all model‑based methods on a household‑object benchmark and all model‑free competitors on a challenging industrial benchmark, while a faster variant, NovaSplat‑Lite, achieves comparable accuracy with a 3.1× speedup.
By Georgios Floros, Evangelos Sartinas, Orestis Vangelis, Maria Pateraki
The paper introduces Ex‑Sim(3)‑Reg, a fast and robust method for pruning 2D‑3D correspondences by reformulating the problem as an extended Sim(3) registration that explicitly accounts for depth noise. The authors provide a theoretical justification and demonstrate that their approach improves registration recall by up to 24.7% on several benchmark datasets, outperforming state‑of‑the‑art baselines. The code for the method is publicly available on GitHub.
By Pei An, Muyao Peng, Junfeng Ding, Jiaqi Yang, Liangliang Nan
arXiv:2606.16569v2 Announce Type: replace
Abstract: Registering two captures of the same indoor space taken at different times underpins persistent spatial memory for robots and AR systems, yet the r...
By Zhiang Chen, Nahyuk Lee, Boyang Sun, Taein Kwon, Marc Pollefeys, Zuria Bauer, Sunghwan Hong
Scal3R is a new method for online 3D reconstruction that addresses the failure of traditional models on long videos by decoupling per‑frame depth from global pose estimation. It reformulates reconstruction as a multi‑reference relative pose query, using lightweight learnable tokens (~1% of parameters) injected into a frozen backbone via asymmetric attention to query poses relative to multiple past keyframes. An online pose‑graph optimization with loop closure further suppresses drift, achieving convergence in 8 hours on a single GPU and reducing average absolute trajectory error by over 60% on KITTI while setting state‑of‑the‑art results on several benchmark datasets.
By Chin-Yang Lin, Yang-Che Sun, Cheng Sun, Fu-En Yang, Min-Hung Chen, Yen-Yu Lin, Wei-Chen Chiu, Yu-Lun Liu