A Pose-only Geometric Constraint for Multi-Camera Pose Adjustment
- URL: http://arxiv.org/abs/2604.23704v1
- Date: Sun, 26 Apr 2026 13:36:46 GMT
- Title: A Pose-only Geometric Constraint for Multi-Camera Pose Adjustment
- Abstract summary: Multi-camera systems offer rich observation capabilities for visual navigation and 3D scene reconstruction.<n>The resulting feature redundancy often compromises computational efficiency.<n>This paper introduces a pose-only geometric constraint for multi-camera systems and proposes a corresponding pose adjustment algorithm.
- Score: 17.926124307402734
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Multi-camera systems offer rich observation capabilities for visual navigation and 3D scene reconstruction; however, the resulting feature redundancy often compromises computational efficiency. This challenge is particularly pronounced during bundle adjustment, where the non-linear optimization of both system poses and scene points incurs substantial computational overhead. To address this challenge, this paper introduces a pose-only geometric constraint for multi-camera systems and proposes a corresponding pose adjustment algorithm. Specifically, we use generalized camera model to establish a unified representation of the multi-camera system. Building upon this model, we formulate the multi-camera pose-only constraint, which implicitly represents a 3D scene point using two base observations and their associated poses, thereby achieving a pose-only representation of the projection geometry. Subsequently, we introduce a multi-camera pose adjustment algorithm that eliminates 3D points from the parameter space, thereby achieving efficient and focused pose optimization. Experimental results on both synthetic and real-world datasets demonstrate that the proposed algorithm outperforms baseline bundle adjustment methods in computational efficiency, while maintaining or even improving pose estimation accuracy.
Related papers
- OmniPoint: Universal Monocular Metric Pointcloud from Any Camera [79.24417597712802]
We present OmniPoint, a unified framework designed to generalize metric reconstruction across diverse imaging sensors.<n>To overcome projection rigidity, OmniPoint abandons conventional planar depth regression.<n>It instead adopts a decoupled ray and distance representation alongside a decoupled training objective.
arXiv Detail & Related papers (2026-09-08T19:45:58Z) - Object Pose Transformer: Unifying Unseen Object Pose Estimation [54.20344997573707]
Learning model-free object pose estimation for unseen instances remains a fundamental challenge in 3D vision.<n>ours jointly predicts depth, point maps, camera parameters, and normalized object coordinates from RGB inputs.<n>ours is camera-agnostic, learning camera intrinsics on-the-fly and supporting optional depth input for metric-scale recovery.
arXiv Detail & Related papers (2026-03-24T16:04:16Z) - Two-Stage Camera Calibration Method for Multi-Camera Systems Using Scene Geometry [52.27010615715154]
This paper presents a novel two-stage calibration method for multi-camera tracking systems.<n>In the first stage, partial calibration of individual cameras is performed based on an operator's annotation of natural geometric primitives.<n>In the second stage, precise system calibration is achieved through interactive manipulation of the projected Effective Field of View.
arXiv Detail & Related papers (2025-12-04T16:47:39Z) - JOGS: Joint Optimization of Pose Estimation and 3D Gaussian Splatting [10.35563602148445]
We propose a unified framework that jointly optimize 3D Gaussian points and camera poses without requiring pre-calibrated inputs.<n>Our approach iteratively refines 3D Gaussian parameters and updates camera poses through a novel co-optimization strategy.<n>Our approach significantly outperforms existing COLMAP-free techniques in reconstruction quality, and also surpasses the standard COLMAP-based baseline in general.
arXiv Detail & Related papers (2025-10-30T04:00:07Z) - Gaussian Alignment for Relative Camera Pose Estimation via Single-View Reconstruction [18.936573991468926]
GARPS is a training-free framework that casts this problem as the direct alignment of two independently reconstructed 3D scenes.<n>It refines an initial pose from a feed-forward two-view pose estimator by optimising a differentiable GMM alignment objective.<n>Experiments on the Real-Estate10K dataset demonstrate that GARPS outperforms both classical and state-of-the-art learning-based methods.
arXiv Detail & Related papers (2025-09-17T02:57:34Z) - A Constrained Optimization Approach for Gaussian Splatting from Coarsely-posed Images and Noisy Lidar Point Clouds [37.043012716944496]
We introduce a constrained optimization method for simultaneous camera pose estimation and 3D reconstruction.<n> Experiments demonstrate that the proposed method significantly outperforms the existing (multi-modal) 3DGS baseline.
arXiv Detail & Related papers (2025-04-12T08:34:43Z) - FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction [69.63414788486578]
FreeSplatter is a scalable feed-forward framework that generates high-quality 3D Gaussians from uncalibrated sparse-view images.<n>Our approach employs a streamlined transformer architecture where self-attention blocks facilitate information exchange.<n>We develop two specialized variants--for object-centric and scene-level reconstruction--trained on comprehensive datasets.
arXiv Detail & Related papers (2024-12-12T18:52:53Z) - 3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction [50.07071392673984]
Existing methods learn 3D rotations parametrized in the spatial domain using angles or quaternions.
We propose a frequency-domain approach that directly predicts Wigner-D coefficients for 3D rotation regression.
Our method achieves state-of-the-art results on benchmarks such as ModelNet10-SO(3) and PASCAL3D+.
arXiv Detail & Related papers (2024-11-01T12:50:38Z) - VICAN: Very Efficient Calibration Algorithm for Large Camera Networks [49.17165360280794]
We introduce a novel methodology that extends Pose Graph Optimization techniques.
We consider the bipartite graph encompassing cameras, object poses evolving dynamically, and camera-object relative transformations at each time step.
Our framework retains compatibility with traditional PGO solvers, but its efficacy benefits from a custom-tailored optimization scheme.
arXiv Detail & Related papers (2024-03-25T17:47:03Z) - iComMa: Inverting 3D Gaussian Splatting for Camera Pose Estimation via Comparing and Matching [14.737266480464156]
We present a method named iComMa to address the 6D camera pose estimation problem in computer vision.
We propose an efficient method for accurate camera pose estimation by inverting 3D Gaussian Splatting (3DGS)
arXiv Detail & Related papers (2023-12-14T15:31:33Z) - Towards Scalable Multi-View Reconstruction of Geometry and Materials [27.660389147094715]
We propose a novel method for joint recovery of camera pose, object geometry and spatially-varying Bidirectional Reflectance Distribution Function (svBRDF) of 3D scenes.
The input are high-resolution RGBD images captured by a mobile, hand-held capture system with point lights for active illumination.
arXiv Detail & Related papers (2023-06-06T15:07:39Z) - Calibrated and Partially Calibrated Semi-Generalized Homographies [65.29477277713205]
We propose the first minimal solutions for estimating the semi-generalized homography given a perspective and a generalized camera.
The proposed solvers are stable and efficient as demonstrated by a number of synthetic and real-world experiments.
arXiv Detail & Related papers (2021-03-11T08:56:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.