arXiv:2610.31797v1 Announce Type: new Abstract: Estimating a camera pose from 3D-2D correspondences - Perspective-n-Point - is one of the most fundamental problems in computer vision. Existing method...
The paper proposes a method for estimating and correcting camera movement in 3D modeling by combining a modified ASIFT feature extractor with a two‑camera rotation correction model and a stereo camera translation estimation model. This integrated approach reduces computational overhead, increases the number of reliable matching points, and improves camera rotation and translation accuracy. Experiments demonstrate a 99.9% match to the actual camera path, outperforming existing state‑of‑the‑art methods and enabling more precise 3D reconstructions.
arXiv:2609.00713v1 Announce Type: new Abstract: Estimation of the absolute pose of an object is an essential task for various robotic applications. Recently, incorporating gravity direction as prior...
The paper introduces a new ellipsoid‑specific framework for camera pose estimation from a single ellipse‑ellipsoid correspondence. It shows that the problem can be reduced to either a position‑only or orientation‑only estimation, with the remaining unknowns solvable in closed form, and further to a single degree‑of‑freedom problem with an analytical pose solution. The authors illustrate the theory with visual examples, discuss practical aspects, and provide source code to aid efficient pose estimation.
The paper introduces a fast Bayesian method for estimating homographies from noisy point correspondences, providing a posterior distribution over the homography parameters. A closed‑form solution for the posterior mean in homogeneous coordinates is derived, complemented by an iterative Bayesian approach to address non‑linearities. Experiments on synthetic data and real image stitching show improved accuracy over DLT and supply uncertainty estimates for the homography.
The paper addresses the lack of standardized evaluation for single‑view novel view synthesis by highlighting the importance of aligning a method’s reference frame with the ground‑truth camera pose. It proposes using traditional 2D‑to‑2D and 2D‑to‑3D image matching to align 3D reconstructions and restricts evaluation to co‑visible regions where a correct ground truth exists. Experiments show that with proper alignment, 3D Gaussian‑based methods reach state‑of‑the‑art performance, and a simple baseline using a video generator and geometry estimator matches specialized novel‑view synthesis models without task‑specific training.
The paper introduces DynaWeightPnP, a real‑time algorithm for correspondence‑free Perspective‑n‑Point (PnP) problems that aligns 3D and 2D shapes without needing point correspondences. It uses a Reproducing Kernel Hilbert Space formulation solved via iterative reweighted least squares, and addresses a numerical ambiguity between rotation and translation with a dynamic weighting sub‑problem and alternative search strategy. Experiments on 3D‑2D vascular centerline registration in endovascular image‑guided interventions show processing rates of 60 Hz (without refinement) and 31 Hz (with refinement) on a single‑core CPU, achieving accuracy comparable to existing methods.
arXiv:2610.33789v1 Announce Type: new Abstract: We present a principled, analytical framework for segmenting multiple independently moving rigid objects during single camera motion, built directly up...
arXiv:2609.10082v1 Announce Type: cross Abstract: Accurate camera intrinsic calibration is fundamental to robot perception, and the accuracy depends on the quality of the collected images. However, e...
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...
The paper introduces a laser‑tracker‑assisted hand‑eye calibration technique for camera‑equipped mobile robots. It integrates 3D metrology from a laser tracker with 2D camera observations, extending a prior method by chaining multiple calibration targets to relax constraints on robot and camera configurations. This generalized formulation enables calibration of the camera pose within the tracker‑localized robot coordinate system for a variety of mobile robot systems.
The paper introduces a unified bundle adjustment framework that jointly optimizes camera parameters, sparse 3D points, and richer geometric features such as lines, coplanarity, and parallelism. It classifies features into scalable ones with direct 2D measurements and higher‑order groups that can be treated as camera‑like entities, allowing group constraints and cross‑feature relations to be expressed via 2D reprojection errors. This approach preserves the sparsity of classical point‑based BA, maintains numerical stability, and achieves runtime comparable to point‑only BA while producing richer 3D structures and improved accuracy.