Single-View Rolling-Shutter SfM

📅 2026-03-12
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🤖 AI Summary
This work addresses the lack of a systematic solution for structure-from-motion (SfM) reconstruction from a single rolling-shutter (RS) camera view. It presents the first geometric model for single-view RS imaging, explicitly characterizing the motion and scene geometry constraints encoded by point and line features in the image. The study identifies the set of recoverable parameters and establishes a minimal problem formulation tailored to this setting. Through careful algebraic derivation and the design of customized solvers, the approach leverages both point and line correspondences to validate its feasibility. Extensive experiments across diverse representative scenes demonstrate the method’s effectiveness while also revealing fundamental theoretical and practical limitations inherent to single-view RS SfM.

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📝 Abstract
Rolling-shutter (RS) cameras are ubiquitous, but RS SfM (structure-from-motion) has not been fully solved yet. This work suggests an approach to remedy this: We characterize RS single-view geometry of observed world points or lines. Exploiting this geometry, we describe which motion and scene parameters can be recovered from a single RS image and systematically derive minimal reconstruction problems. We evaluate several representative cases with proof-of-concept solvers, highlighting both feasibility and practical limitations.
Problem

Research questions and friction points this paper is trying to address.

Rolling-shutter
Structure-from-Motion
Single-view geometry
Camera motion
3D reconstruction
Innovation

Methods, ideas, or system contributions that make the work stand out.

Rolling-shutter
Single-view geometry
Structure-from-Motion
Minimal solvers
Camera motion recovery
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S
Sofía Errázuriz Muñoz
KTH Royal Institute of Technology, Sweden
K
Kim Kiehn
KTH Royal Institute of Technology, Sweden
P
Petr Hruby
KTH Royal Institute of Technology, Sweden
Kathlén Kohn
Kathlén Kohn
Associate Professor at KTH
algebraic geometrymachine learningcomputer visionstatistics