Self-Calibration Supported Robust Projective Structure-from-Motion

• URL: http://arxiv.org/abs/2007.02045v1
• Date: Sat, 4 Jul 2020 08:47:10 GMT
• Title: Self-Calibration Supported Robust Projective Structure-from-Motion
• Authors: Rui Gong, Danda Pani Paudel, Ajad Chhatkuli, and Luc Van Gool
• Abstract summary: We propose a unified Structure-from-Motion (SfM) method, in which the matching process is supported by self-calibration constraints. We show experimental results demonstrating robust multiview matching and accurate camera calibration by exploiting these constraints.
• Score: 80.15392629310507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Typical Structure-from-Motion (SfM) pipelines rely on finding correspondences across images, recovering the projective structure of the observed scene and upgrading it to a metric frame using camera self-calibration constraints. Solving each problem is mainly carried out independently from the others. For instance, camera self-calibration generally assumes correct matches and a good projective reconstruction have been obtained. In this paper, we propose a unified SfM method, in which the matching process is supported by self-calibration constraints. We use the idea that good matches should yield a valid calibration. In this process, we make use of the Dual Image of Absolute Quadric projection equations within a multiview correspondence framework, in order to obtain robust matching from a set of putative correspondences. The matching process classifies points as inliers or outliers, which is learned in an unsupervised manner using a deep neural network. Together with theoretical reasoning why the self-calibration constraints are necessary, we show experimental results demonstrating robust multiview matching and accurate camera calibration by exploiting these constraints.

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