We propose a new method for recognizing the pose of objects from a single image that for learning uses only unlabelled videos and a weak empirical prior on the object poses. Video frames differ primarily in the pose of the objects they contain, so our method distils the pose information by analyzing the differences between frames. The distillation uses a new dual representation of the geometry of objects as a set of 2D keypoints, and as a pictorial representation, i.e. a skeleton image. This has three benefits: (1) it provides a tight 'geometric bottleneck' which disentangles pose from appearance, (2) it can leverage powerful image-to-image translation networks to map between photometry and geometry, and (3) it allows to incorporate empirical pose priors in the learning process. The pose priors are obtained from unpaired data, such as from a different dataset or modality such as mocap, such that no annotated image is ever used in learning the pose recognition network. In standard benchmarks for pose recognition for humans and faces, our method achieves state-of-the-art performance among methods that do not require any labelled images for training
Learning deformable 3D objects from 2D images is an extremely ill-posed problem. Existing methods rely on explicit supervision to establish multi-view correspondences, such as template shape models and keypoint annotations, which restricts their applicability on objects "in the wild". In this paper, we propose to use monocular videos, which naturally provide correspondences across time, allowing us to learn 3D shapes of deformable object categories without explicit keypoints or template shapes. Specifically, we present DOVE, which learns to predict 3D canonical shape, deformation, viewpoint and texture from a single 2D image of a bird, given a bird video collection as well as automatically obtained silhouettes and optical flows as training data. Our method reconstructs temporally consistent 3D shape and deformation, which allows us to animate and re-render the bird from arbitrary viewpoints from a single image. IntroductionRecently, with the adoption of machine learning, reconstructing 3D shapes from 2D images has advanced considerably. While this task traditionally requires establishing correspondences between multiple views [13], learning-based approaches have demonstrated the possibility of inferring 3D shapes from a single image, by learning priors for a specific object category [
The article deals with accuracy of machine tools. The aim was to determine the accuracy with which already operates used machine – HAAS CNC Milling machine MiniMill. For evaluation of accuracy of the machine tool was used Renishaw QC20-W Ballbar system, which has the Department of Machining, Assembly and Engineering Metrology of VŠB – TUO. The theoretical part describes the measurement principle using a telescopic Ballbar system. The principle of measurement is the comparison of the actual deviation from the ideal circle which is programmed. Experimental part is focused on the diagnosis of the whole process by the direct method of measurement during changing feed rate. Specifically, the determination of circularity deviation at machine tool was in accordance with international standards, e.g. ISO 230-4.
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