arXiv:2509.06285v2 Announce Type: cross
Abstract: LiDAR point cloud registration is fundamental to robotic perception and navigation. In geometrically degenerate environments (e.g., corridors), regis...
By Xiangcheng Hu, Xieyuanli Chen, Mingkai Jia, Jin Wu, Ping Tan, Steven L. Waslander
DMM-Align introduces a closed‑loop framework for 2D‑3D registration that jointly refines correspondences, estimates pose, and learns representations using a shared differentiable geometric state. The method employs two diffusion processes: a geometry‑aware diffusion that improves the soft matching matrix for robust correspondence estimation, and a geometry‑conditioned diffusion teacher that feeds pose‑induced supervision back into feature learning. Experiments on 7‑Scenes and RGB‑D Scenes V2 show that DMM‑Align outperforms strong baselines, particularly in low‑overlap and heavily occluded scenarios, demonstrating the value of closed‑loop geometric feedback.
By Chongjian Wang, Junjie Gao
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
FlowMoDE introduces a coarse‑to‑fine flow‑matching framework for monocular depth estimation that balances efficiency and detail reconstruction. The method first decodes coarse depth conditioned on diffused features from a pre‑trained semantic encoder, then refines fine structural patterns with expanded decoding layers. Trained only on a synthetic indoor dataset, FlowMoDE achieves a 14.1% lower relative error and a 9.9% lower Chamfer distance compared to state‑of‑the‑art methods across five real‑world indoor and outdoor datasets, and demonstrates strong generalization to realistic robotic scenes.
By Liangjing Shao, Wanhao Liu, Jinsong Lin, Zhiwei Fang, Hongliang Ren
The paper introduces a high‑resolution Rectified Flow Matching model trained on 256×256 chest CT images to serve as a generative prior for CT reconstruction. A two‑stage training strategy—initial strong anatomically informed augmentation followed by fine‑tuning—helps mitigate overfitting and improve structural fidelity. When evaluated on various CT inverse problems, Flow Matching‑based reconstruction methods outperform diffusion‑based algorithms in PSNR, SSIM, and perceptual quality while requiring fewer sampling steps.
By Davide Evangelista
The paper investigates test‑time adaptation (TTA) techniques for 3D point‑cloud registration in laparoscopic surgery, where synthetic training data must be adapted to noisy, sparse, and occluded real intraoperative reconstructions. It adapts three families of TTA methods—model, normalization, and input adaptation—to handle asymmetric shifts between preoperative meshes and intraoperative clouds, replacing classification‑based entropy objectives with correspondence‑based ones. Experiments on synthetic and real targets show that input adaptation consistently reduces registration error with low inference latency, making it the most promising approach for surgical applications.
By Nina Bodelot, Soufiane Belharbi, Eric Granger