Year: 2026
Type: preprint
Abstract: Relative camera pose geometry, formulated via fundamental matrix estimation, is a challenging problem in many robotics and VR/AR applications. These applications occasionally contain fast monocular camera motion, which severely blurs the image and prevents the use of traditional multi-view geometry methods for camera pose estimation. To handle these cases, we propose GeoBlur, a framework for estimating the fundamental matrix and recovering relative camera pose directly from a single motion-blurred image, using the motion cues from blur artifacts. GeoBlur first predicts the visual correspondences between two time instances within the camera exposure window; then, it infers the fundamental matrix by solving the single-frame epipolar geometry problem under time-direction ambiguity. The resulting fundamental matrix is unique up to transposition, reflecting the inherent ambiguity in the direction of time. GeoBlur improves performance on synthetic and hybrid benchmarks while remaining competitive with prior work on real motion-blur data. We further demonstrate the use of GeoBlur on the downstream task on single frame motion segmentation.
Source: arXiv (Cornell University)
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7160244161
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