Abstract: Systems and techniques are provided for registering three-dimensional (3D) images to deformable models.

Abstract: Systems and techniques are provided for modeling three-dimensional (3D) meshes using images. An example method can include receiving, via a neural network system, an image of a target and metadata associated with the image and/or a device that captured the image; determining, based on the image and metadata, first 3D mesh parameters of a first 3D mesh of the target, the first 3D mesh parameters and first 3D mesh corresponding to a first reference frame associated with the image and/or the device; and determining, based on the first 3D mesh parameters, second 3D mesh parameters for a second 3D mesh of the target, the second 3D mesh parameters and second 3D mesh corresponding to a second reference frame, the second reference frame including a 3D coordinate system of a real-world scene where the target is located.

Abstract: Media processing systems and techniques are described. A media processing system receives image data that represents an environment captured by an image sensor. The media processing system receives an indication of an object in the environment that is represented in the image data. The media processing system divides the image data into regions, including a first region and a second region. The object is represented in one of the plurality of regions. The media processing system modifies the image data to obscure the first region without obscuring the second region based on the object being represented in the one of the plurality of regions. The media processing system outputs the image data after modifying the image data. In some examples, the object is depicted in the first region and not the second region. In some examples, the object is depicted in the second region and not the first region.

Abstract: Methods, systems, and apparatuses are provided to automatically detect objects within images. For example, an image capture device may capture an image, and may apply a trained neural network to the image to generate an object value and a class value for each of a plurality of portions of the image. Further, the image capture device may determine, for each of the plurality of image portions, a confidence value based on the object value and the class value corresponding to each image portion. The image capture device may also detect an object within at least one image portion based on the confidence values. Further, the image capture device may output a bounding box corresponding to the at least one image portion. The bounding box defines an area of the image that includes one or more objects.

Abstract: Systems and techniques are provided for modeling three-dimensional (3D) meshes using images. An example method can include receiving, via a neural network system, an image of a target and metadata associated with the image and/or a device that captured the image; determining, based on the image and metadata, first 3D mesh parameters of a first 3D mesh of the target, the first 3D mesh parameters and first 3D mesh corresponding to a first reference frame associated with the image and/or the device; and determining, based on the first 3D mesh parameters, second 3D mesh parameters for a second 3D mesh of the target, the second 3D mesh parameters and second 3D mesh corresponding to a second reference frame, the second reference frame including a 3D coordinate system of a real-world scene where the target is located.

Abstract: Systems and techniques are provided for modeling three-dimensional (3D) meshes using multi-view image data. An example method can include determining, based on a first image of a target, first 3D mesh parameters for the target corresponding to a first coordinate frame; determining, based on a second image of the target, second 3D mesh parameters for the target corresponding to a second coordinate frame; determining third 3D mesh parameters for the target in a third coordinate frame, the third 3D mesh parameters being based on the first and second 3D mesh parameters and relative rotation and translation parameters of image sensors that captured the first and second images; determining a loss associated with the third 3D mesh parameters, the loss being based on the first and second 3D mesh parameters and the relative rotation and translation parameters; determining 3D mesh parameters based on the loss and third 3D mesh parameters.

Abstract: Systems and techniques are provided for registering three-dimensional (3D) images to deformable models.

Abstract: A method performed by an electronic device is described. The method includes generating a depth map of a scene external to a vehicle. The method also includes performing first processing in a first direction of a depth map to determine a first non-obstacle estimation of the scene. The method also includes performing second processing in a second direction of the depth map to determine a second non-obstacle estimation of the scene. The method further includes combining the first non-obstacle estimation and the second non-obstacle estimation to determine a non-obstacle map of the scene. The combining includes combining comprises selectively using a first reliability map of the first processing and/or a second reliability map of the second processing The method additionally includes navigating the vehicle using the non-obstacle map.

Abstract: A classifier for detecting objects in images can be configured to receive features of an image from a feature extractor. The classifier can determine a feature window based on the received features, and allows access by each decision tree of the classifier to only a predetermined area of the feature window. Each decision tree of the classifier can compare a corresponding predetermined area of the feature window with one or more thresholds. The classifier can determine an object in the image based on the comparisons. In some examples, the classifier can determine objects in a feature window based on received features, where the received features are based on color information for an image.

Abstract: A method performed by an electronic device is described. The method includes generating a depth map of a scene external to a vehicle. The method also includes performing first processing in a first direction of a depth map to determine a first non-obstacle estimation of the scene. The method also includes performing second processing in a second direction of the depth map to determine a second non-obstacle estimation of the scene. The method further includes combining the first non-obstacle estimation and the second non-obstacle estimation to determine a non-obstacle map of the scene. The combining includes combining comprises selectively using a first reliability map of the first processing and/or a second reliability map of the second processing The method additionally includes navigating the vehicle using the non-obstacle map.

Abstract: A method performed by an electronic device is described. The method includes obtaining a motion vector map based on at least two images. The motion vector map has fewer motion vectors than a number of pixels in each of the at least two images. The method also includes obtaining a feature point from one of the at least two images. The method further includes performing a matching operation between a template associated with the feature point and at least one search space based on the motion vector map. The method additionally includes determining a motion vector corresponding to the feature point based on the matching operation.

Abstract: A method performed by an electronic device is described. The method includes generating a depth map of a scene external to a vehicle. The method also includes performing first processing in a first direction of a depth map to determine a first non-obstacle estimation of the scene. The method also includes performing second processing in a second direction of the depth map to determine a second non-obstacle estimation of the scene. The method further includes combining the first non-obstacle estimation and the second non-obstacle estimation to determine a non-obstacle map of the scene. The combining includes combining comprises selectively using a first reliability map of the first processing and/or a second reliability map of the second processing The method additionally includes navigating the vehicle using the non-obstacle map.

Abstract: A method performed by an electronic device is described. The method includes generating a depth map of a scene external to a vehicle. The method also includes performing first processing in a first direction of a depth map to determine a first non-obstacle estimation of the scene. The method also includes performing second processing in a second direction of the depth map to determine a second non-obstacle estimation of the scene. The method further includes combining the first non-obstacle estimation and the second non-obstacle estimation to determine a non-obstacle map of the scene. The combining includes combining comprises selectively using a first reliability map of the first processing and/or a second reliability map of the second processing The method additionally includes navigating the vehicle using the non-obstacle map.

Abstract: A method performed by an electronic device is described. The method includes obtaining a motion vector map based on at least two images. The motion vector map has fewer motion vectors than a number of pixels in each of the at least two images. The method also includes obtaining a feature point from one of the at least two images. The method further includes performing a matching operation between a template associated with the feature point and at least one search space based on the motion vector map. The method additionally includes determining a motion vector corresponding to the feature point based on the matching operation.

Abstract: A method performed by an electronic device is described. The method includes generating a depth map of a scene external to a vehicle. The method also includes performing first processing in a first direction of a depth map to determine a first non-obstacle estimation of the scene. The method also includes performing second processing in a second direction of the depth map to determine a second non-obstacle estimation of the scene. The method further includes combining the first non-obstacle estimation and the second non-obstacle estimation to determine a non-obstacle map of the scene. The combining includes combining comprises selectively using a first reliability map of the first processing and/or a second reliability map of the second processing The method additionally includes navigating the vehicle using the non-obstacle map.

Abstract: A method performed by an electronic device is described. The method includes performing vertical processing of a depth map to determine a vertical non-obstacle estimation. The method also includes performing horizontal processing of the depth map to determine a horizontal non-obstacle estimation. The method further includes combining the vertical non-obstacle estimation and the horizontal non-obstacle estimation. The method additionally includes generating a non-obstacle map based on the combination of the vertical and horizontal non-obstacle estimations.

Abstract: This disclosure describes methods and apparatus for decoding data. In one aspect, the method comprises decoding encoded video data to obtain decoded video frame data, the encoded video data comprising encoded video frame data encoded at a first frame rate and embedded data. The method further comprises determining a camera parameter from the embedded data and up-converting the decoded video frame data to a second frame rate based on the camera parameter. The determined camera parameter may be, for example, a parameter associated with one or more of a zoom factor, an auto focus status, lens position information, frame luma information, an auto exposure (AE) convergence status, an automatic white balance (AWB) convergence status, global motion information, and frame blurriness information, and the like. An encoding device may embed the camera parameter(s) in an encoded video bit stream for a decoder to utilize during frame rate up-conversion.

Abstract: A method performed by an electronic device is described. The method includes performing vertical processing of a depth map to determine a vertical non-obstacle estimation. The method also includes performing horizontal processing of the depth map to determine a horizontal non-obstacle estimation. The method further includes combining the vertical non-obstacle estimation and the horizontal non-obstacle estimation. The method additionally includes generating a non-obstacle map based on the combination of the vertical and horizontal non-obstacle estimations.

Abstract: A system and method for correcting artifacts present in video frames is disclosed. The system includes a decision module (110) and an artifact correction unit (120). The system receives artifact metrics (112) and artifact maps (122) corresponding to artifacts present in video frames and prioritizes the video frames to be corrected by considering the type and severity of the artifacts and the content of the video frames (113). Subsequent to prioritizing the video frames, the system corrects artifacts of selected frames by adjusting compressed domain parameters (126), such as quantization parameters and mode decisions. An encoder (130) compresses the video frames according to the adjusted compressed domain parameter to automatically provide a video stream with a lower incidence and severity of artifacts.

Abstract: A method performed by an electronic device is described. The method includes generating a plurality of bounding regions based on an image. The method also includes determining a subset of the plurality of bounding regions based on at least one criterion and a selected area in the image. The method further includes processing the image based on the subset of the plurality of bounding regions.