Abstract: A computer-implemented method of augmented reality includes capturing the video flux with a video camera, extracting, from the video flux, one or more 2D images each representing the real object, and obtaining a 3D model representing the real object. The method also includes determining a pose of the 3D model relative to the video flux, among candidate poses. The determining rewards a mutual information, for at least one 2D image and for each given candidate pose, which represents a mutual dependence between a virtual 2D rendering and the at least one 2D image. The method also includes augmenting the video flux based on the pose. This forms an improved solution of augmented reality for augmenting a video flux of a real scene including a real object.

Abstract: A computer-implemented method for segmenting an object in at least one image acquired by a camera including computing an edge probabilities image based on the image, said edge probabilities image comprising, for each pixel of the image, the probability that said pixel is an edge, computing a segmentation probabilities image based on the image (IM), said segmentation probabilities image comprising, for each pixel of the image (IM), the probability that said pixel belongs to the object (OBJ), and computing a binary mask of the object based on the edge probabilities image and based on the segmentation probabilities image.

Abstract: A computer-implemented method of augmented reality includes capturing the video flux with a video camera, extracting, from the video flux, one or more 2D images each representing the real object, and obtaining a 3D model representing the real object. The method also includes determining a pose of the 3D model relative to the video flux, among candidate poses. The determining rewards a mutual information, for at least one 2D image and for each given candidate pose, which represents a mutual dependence between a virtual 2D rendering and the at least one 2D image. The method also includes augmenting the video flux based on the pose. This forms an improved solution of augmented reality for augmenting a video flux of a real scene including a real object.

Abstract: A computer-implemented method of computer vision in a scene that includes one or more transparent objects and/or one or more reflecting objects comprises obtaining a plurality of images of the scene, each image corresponding to a respective acquisition of a physical signal, the plurality of images including at least two images corresponding to different physical signals; and generating a segmented image of the scene based on the plurality of images. This improves the field of computer vision.

Abstract: Described is a computer-implemented method for learning a function configured for reconstructing, for a class of real objects, a 3D modeled object that represents an instance of the class from a depth map of the instance. The method comprises providing a parametric model of the class, creating a database, and learning the function with the database. The method improves the 3D reconstruction of a real object from a depth map.

Abstract: The invention notably relates to a computer-implemented method for learning a function configured for reconstructing, for a class of real objects, a 3D modeled object that represents an instance of the class from a depth map of the instance. The method comprises providing a parametric model of the class, creating a database, and learning the function with the database. The method improves the 3D reconstruction of a real object from a depth map.

Abstract: A computer-implemented method of computer vision in a scene that includes one or more transparent objects and/or one or more reflecting objects comprises obtaining a plurality of images of the scene, each image corresponding to a respective acquisition of a physical signal, the plurality of images including at least two images corresponding to different physical signals; and generating a segmented image of the scene based on the plurality of images. This improves the field of computer vision.

Abstract: Described is a computer-implemented method for learning a function configured for reconstructing, for a class of real objects, a 3D modeled object that represents an instance of the class from a depth map of the instance. The method comprises providing a parametric model of the class, creating a database, and learning the function with the database. The method improves the 3D reconstruction of a real object from a depth map.

Abstract: The invention notably relates to a computer-implemented method for reconstructing a 3D modeled object that represents a real object, from a 3D mesh and measured data representative of the real object, the method comprising providing a set of deformation modes; determining a composition of the deformation modes which optimizes a program that rewards fit between the 3D mesh as deformed by the composition and the measured data, and that further rewards sparsity of the deformation modes involved in the determined composition; and applying the composition to the 3D mesh. The method improves reconstructing a 3D modeled object that represents a real object.

Abstract: The invention notably relates to a computer-implemented method for learning a function configured for reconstructing, for a class of real objects, a 3D modeled object that represents an instance of the class from a depth map of the instance. The method comprises providing a parametric model of the class, creating a database, and learning the function with the database. The method improves the 3D reconstruction of a real object from a depth map.

Abstract: The invention notably relates to a computer-implemented method for designing a 3D modeled object representing a real object. The method comprises the steps of providing a 3D representation of the real object, identifying occurrences of a geometric feature at 3D positions of the 3D representation, providing at least one 2D view of the real object, identifying occurrences of a graphic feature at 2D positions of the 2D view, the geometric feature corresponding to the graphic feature, computing camera parameters that minimize a distance between a set of projections of the 3D positions on the 2D view and a set of 2D positions. This provides an improved solution for designing a 3D modeled object representing a real object.

Abstract: The invention notably relates to a computer-implemented method for reconstructing a 3D modeled object that represents a real object, from a 3D mesh and measured data representative of the real object, the method comprising providing a set of deformation modes; determining a composition of the deformation modes which optimizes a program that rewards fit between the 3D mesh as deformed by the composition and the measured data, and that further rewards sparsity of the deformation modes involved in the determined composition; and applying the composition to the 3D mesh. The method improves reconstructing a 3D modeled object that represents a real object.

Abstract: The invention notably relates to a computer-implemented method for designing a 3D modeled object representing a real object. The method comprises the steps of providing a 3D representation of the real object, identifying occurrences of a geometric feature at 3D positions of the 3D representation, providing at least one 2D view of the real object, identifying occurrences of a graphic feature at 2D positions of the 2D view, the geometric feature corresponding to the graphic feature, computing camera parameters that minimize a distance between a set of projections of the 3D positions on the 2D view and a set of 2D positions. This provides an improved solution for designing a 3D modeled object representing a real object.

Abstract: A method for synchronizing at least two video streams originating from at least two cameras having a common visual field. The method includes acquiring the video streams and recording of the images composing each video stream on a video recording medium; rectifying the images of the video streams along epipolar lines; extracting an epipolar line from each rectified image of each video stream; composing an image of a temporal epipolar line for each video stream; computing a temporal shift value ? between the video streams by matching the images of a temporal epipolar line of each video stream for each epipolar line of the video streams; computing a temporal desynchronization value Dt between the video streams by taking account of the temporal shift values 6 computed for each epipolar line of the video streams; synchronizing the video streams by taking into account the computed temporal desynchronization value Dt.