BINDER is a new probabilistic model for medical image registration that builds on mutual information and uses latent voxel‑wise correspondences to enable closed‑form iterative updates. The approach yields a demons‑like optimization algorithm that performs robustly on both monomodal and multimodal tasks, and a sampler that quantifies uncertainty in high‑dimensional 3D deformations. The authors provide the code on GitHub for public use.
By Stefano Cerri, Amirhossein Hassankhani, Ya\"el Balbastre, Koen Van Leemput
The paper presents a fully Bayesian framework for reconstructing closed curves from point‑cloud data, treating observed points as noisy perturbations of latent locations constrained to lie on an underlying curve. A non‑parametric prior regularizes the curve, and posterior inference is performed with Markov chain Monte Carlo samplers designed for point‑cloud characteristics. Experiments on synthetic and real LiDAR datasets demonstrate accurate reconstructions and quantified uncertainty over the recovered curves.
By Asir Intesar Tushar, Ioannis Sgouralis
arXiv:2609.13691v1 Announce Type: new
Abstract: Most existing visual odometry (VO) systems treat feature correspondences as deterministic measurements or assign uniform uncertainty, ignoring the inhe...
By Yuqing Wang, Xiaoji Niu, Yan Wang, Hailiang Tang, Jian Kuang, Tisheng Zhang
arXiv:2608. 07116v1 Announce Type: cross Abstract: Camera localization in bronchoscopy remains a challenging problem due to stringent accuracy requirements, real-time constraints, and limited training data.
By Lumin Chen, Qingyao Tian, Jinpeng Li, Haoyu Jiang, Huai Liao, Xinyan Huang, Hongbin Liu, Dong Yi
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
The paper introduces SAM‑H, a planar object tracker that estimates 8‑degree‑of‑freedom homographies directly from segmentation mask contours using a training‑free pipeline. When applied to masks from SAM 2, SAM‑H achieves a new state‑of‑the‑art performance on the PlanarTrack benchmark, improving the p@5 metric by 18.4 percentage points. The authors also demonstrate that combining segmentation‑based and correspondence‑based homography estimation yields WOFTSAM, which surpasses all previous methods on both PlanarTrack and POT‑210, and provide precise re‑annotations of PlanarTrack initial poses for more accurate benchmarking.
By Jonas Serych, Jiri Matas
arXiv:2606. 13509v1 Announce Type: cross Abstract: Indoor vision-based localization systems are affected by detection noise, occlusions, and limited camera coverage, leading to uncertainty at multiple stages of the pipeline.
By Mateo Toro Diz, Jonathan Hoss, Noah Klarmann
Precise multi-camera tracking traditionally relies on rigorous 3D site calibration, yet this requirement is often operationally impossible in large-scale deployments. Privacy regulations frequently pr...
The paper introduces an on-the-fly homography calibration system for multi-camera tracking that starts from coarse manual homographies and refines them using a centroid-based projection optimization (PO) on live detection metadata. PO continuously aligns ground-plane geometry without adding computational latency, enabling the system to adapt automatically to camera movements or environmental changes. The refined geometry feeds a bird's-eye-view tracker that fuses detections and unifies trajectories across zones while maintaining privacy safety and zero overhead.
By David Voihanski, Mor Sinai, Ben Zion Bobrovsky
Gaussian splatting is the current state-of-the-art for dense, deformable 3D anatomy reconstruction in robot-assisted minimally invasive surgery (RAMIS); however, most pipelines are offline and depend on accurate camera trajectory priors (often from robotic kinematics), limiting applicability when priors are missing or noisy. To address these limitations, we propose Track2Map, an online 3D Gaussian Splatting pipeline that jointly optimizes camera trajectory and 3D deformable scene representation directly from surgical video.
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:2609.25746v1 Announce Type: cross
Abstract: ICP-based 3D Gaussian Splatting (3DGS) SLAM tracks in real time by registering incoming frames against map Gaussians, using each primitive's covarian...
By Edward Beng Wai Tan, Siew-Kei Lam