arXiv Computer Vision
Oct 26

Camera Movement Estimation and Path Correction using the Combination of Modified A-SIFT and Stereo System for 3D Modelling

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.

By Usha Kumari, Shuvendu Rana
arXiv Computer Vision
Oct 12

Perspective-1-Ellipsoid: Formulation, Analysis and Solutions of the Camera Pose Estimation Problem from One Ellipse-Ellipsoid Correspondence

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.

By Vincent Gaudilli\`ere, Gilles Simon, Marie-Odile Berger
arXiv Computer Vision
Sep 15

Closed-form Bayesian homography estimation from noisy point correspondences

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.

By Hanne Beuter, Sebastian Dorn
arXiv AI
Oct 31

Aligning Misaligned Benchmarks for Single-View Novel View Synthesis

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.

By Yang-Che Tseng, Erqun Dong, Jeong-gi Kwak, Gopal Sharma, Shakiba Kheradmand, Stephan Garbin, Hossam Isack, Kwang Moo Yi