Kabushiki Kaisha Toshiba

Microsoft Technology Licensing, LLC

Christopher Zach

Shahram Izadi

Jamie Daniel Joseph Shotton

Andrew William Fitzgibbon

Antonio Criminisi

Christoph Rhemann

Stephan Liwicki

Benjamin Michael Glocker

Steven Nabil Bathiche

Michael Bleyer

Vivek Pradeep

David Kim

Rudra Prasad Poudel Karmatha

Ujwal Bonde

Pulak Purkait

Camera or object pose calculation is described, for example, to relocalize a mobile camera (such as on a smart phone) in a known environment or to compute the pose of an object moving relative to a fixed camera. The pose information is useful for robotics, augmented reality, navigation and other applications. In various embodiments where camera pose is calculated, a trained machine learning system associates image elements from an image of a scene, with points in the scene's 3D world coordinate frame. In examples where the camera is fixed and the pose of an object is to be calculated, the trained machine learning system associates image elements from an image of the object with points in an object coordinate frame. In examples, the image elements may be noisy and incomplete and a pose inference engine calculates an accurate estimate of the pose.

Camera/object pose from predicted coordinates

An image processing method for segmenting an image, the method comprising:

Computer vision system and method

A camera pose estimation method determines the translation and rotation between a first camera pose and a second camera pose. Features are extracted from a first image captured at the first position and a second image captured at the second position, the extracted features comprising location, scale information and a descriptor, the descriptor comprising information that allows a feature from the first image to be matched with a feature from the second image. Features are matched between the first image and the second image. The depth ratio of matched features is determined from the scale information. n matched features are selected, where at least one of the matched features is selected with both the depth ratio and location. The translation and rotation are calculated between the first camera pose and the second camera pose using the selected matched features with depth ratio derived from the scale information.

Camera pose estimating method and system

A method of compensating for camera motion during capture of the image in a rolling shutter camera, the method comprising:

System and method for motion compensation in images

A method for adjusting an image using message passing comprises associating each pixel of an image with a node of a graph and one or more cliques of nodes, determining for a node of the graph a respective set of possible pixel labels for which a unary potential is known, computing for that node a unary potential of a possible pixel label for which the unary potential is unknown, adjusting a clique potential associated with each clique to which that node belongs based on the unary potentials, and adjusting, based on the adjusted clique potential associated with each clique to which that node belongs, at least one of the messages between that node and the other nodes of each clique. Once a convergence criterion is met, an adjusted image is produced having pixel labels determined from the adjusted messages.

Image adjustment

A method for use in estimating a pose of an imaged object comprises identifying candidate elements of an atlas that correspond to pixels in an image of the object, forming pairs of candidate elements, and comparing the distance between the members of each pair and with the distance between the corresponding pixels.

Object pose recognition

A combination of three computational components may provide memory and computational efficiency while producing results with little latency, e.g., output can begin with the second frame of video being processed. Memory usage may be reduced by maintaining key frames of video and pose information for each frame of video. Additionally, only one global volumetric structure may be maintained for the frames of video being processed. To be computationally efficient, only depth information may be computed from each frame. Through fusion of multiple depth maps from different frames into a single volumetric structure, errors may average out over several frames, leading to a final output with high quality.

Real-time three-dimensional reconstruction of a scene from a single camera

A computer-implemented stereo image processing method which uses contours is described. In an embodiment, contours are extracted from two silhouette images captured at substantially the same time by a stereo camera of at least part of an object in a scene. Stereo correspondences between contour points on corresponding scanlines in the two contour images (one corresponding to each silhouette image in the stereo pair) are calculated on the basis of contour point comparison metrics, such as the compatibility of the normal of the contours and/or a distance along the scanline between the point and a centroid of the contour. A corresponding system is also described.

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Cambridge, United Kingdom

Christopher Zach