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Current deep models provide remarkable object detection in terms of objectclassification and localization. However, estimating object rotation withrespect to other visual objects in the visual context of an input image stilllacks deep studies due to the unavailability of object datasets with rotationannotations.  This paper tackles these two challenges to solve the rotation estimation of aparked bike with respect to its parking area. First, we leverage the power of3D graphics to build a camera-agnostic well-annotated Synthetic Bike RotationDataset (SynthBRSet). Then, we propose an object-to-spot rotation estimator(OSRE) by extending the object detection task to further regress the bikerotations in two axes. Since our model is purely trained on synthetic data, weadopt image smoothing techniques when deploying it on real-world images. Theproposed OSRE is evaluated on synthetic and real-world data providing promisingresults. Our data and code are available at\href{https://github.com/saghiralfasly/OSRE-Project}{https://github.com/saghiralfasly/OSRE-Project}.

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