Gaussian Alignment for Relative Camera Pose Estimation via Single-View Reconstruction

• URL: http://arxiv.org/abs/2509.13652v1
• Date: Wed, 17 Sep 2025 02:57:34 GMT
• Title: Gaussian Alignment for Relative Camera Pose Estimation via Single-View Reconstruction
• Authors: Yumin Li, Dylan Campbell,
• Abstract summary: 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.
• Score: 18.936573991468926
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Estimating metric relative camera pose from a pair of images is of great importance for 3D reconstruction and localisation. However, conventional two-view pose estimation methods are not metric, with camera translation known only up to a scale, and struggle with wide baselines and textureless or reflective surfaces. This paper introduces GARPS, a training-free framework that casts this problem as the direct alignment of two independently reconstructed 3D scenes. GARPS leverages a metric monocular depth estimator and a Gaussian scene reconstructor to obtain a metric 3D Gaussian Mixture Model (GMM) for each image. It then refines an initial pose from a feed-forward two-view pose estimator by optimising a differentiable GMM alignment objective. This objective jointly considers geometric structure, view-independent colour, anisotropic covariance, and semantic feature consistency, and is robust to occlusions and texture-poor regions without requiring explicit 2D correspondences. Extensive experiments on the Real\-Estate10K dataset demonstrate that GARPS outperforms both classical and state-of-the-art learning-based methods, including MASt3R. These results highlight the potential of bridging single-view perception with multi-view geometry to achieve robust and metric relative pose estimation.

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