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OSRE: Object-to-Spot Rotation Estimation for Bike Parking Assessment

arXiv 2303.00725 ↗First posted 1 Mar 2023cs.CVcited by 0

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Abstract (arXiv)

Current deep models provide remarkable object detection in terms of object classification and localization. However, estimating object rotation with respect to other visual objects in the visual context of an input image still lacks deep studies due to the unavailability of object datasets with rotation annotations. This paper tackles these two challenges to solve the rotation estimation of a parked bike with respect to its parking area. First, we leverage the power of 3D graphics to build a camera-agnostic well-annotated Synthetic Bike Rotation Dataset (SynthBRSet). Then, we propose an object-to-spot rotation estimator (OSRE) by extending the object detection task to further regress the bike rotations in two axes. Since our model is purely trained on synthetic data, we adopt image smoothing techniques when deploying it on real-world images. The proposed OSRE is evaluated on synthetic and real-world data providing promising results. Our data and code are available at hrefhttps://github.com/saghiralfasly/OSRE-Projecthttps://github.com/saghiralfasly/OSRE-Project.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 46 of its 71 references that are in the map. First posted on arXiv 1 Mar 2023.

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