Abstract: A system and method are presented for estimating the orientation of a panoramic camera mounted on a vehicle relative to the vehicle coordinate frame. An initial pose estimate of the vehicle is determined based on global positioning system data, inertial measurement unit data, and wheel odometry data of the vehicle. Image data from images captured by the camera is processed to obtain one or more tracks, each track including a sequence of matched feature points stemming from a same three-dimensional location. A correction parameter determined from the initial pose estimate and tracks can then be used to correct the orientations of the images captured by the camera. The correction parameter can be optimized by deriving a correction parameter for each of a multitude of distinct subsequences of one or more runs. Statistical analysis can be performed on the determined correction parameters to produce robust estimates.

Abstract: A method and system are provided for image matching using an appearance augmented 3-D point cloud that includes receiving a first and second posed image, extracting image features from the first and second posed image, and comparing the extracted image features from the first posed image with the extracted image features from the second posed image. Next, a comparison is done to identify one or more matched features of the first and second posed images based on a feature appearance, where a grouping of the matched features of the first and second posed images is performed where the grouped matched features are associated with a first three-dimensional point. A position of the first three-dimensional point is identified based on a positional triangulation of the grouped matched features associated with the first three-dimensional point, and the first three-dimensional point is then augmented with the grouped matched features.

Abstract: Motion capture animation, shape completion and markerless motion capture methods are provided. A pose deformation space model encoding variability in pose is learnt from a three-dimensional (3D) dataset. Body shape deformation space model encoding variability in pose and shape is learnt from another 3D dataset. The learnt pose model is combined with the learnt body shape model. For motion capture animation, given parameter set, the combined model generates a 3D shape surface of a body in a pose and shape. For shape completion, given partial surface of a body defined as 3D points, the combined model generates a 3D surface model in the combined spaces that fits the 3D points. For markerless motion capture, given 3D information of a body, the combined model traces the movement of the body using the combined spaces that fits the 3D information or reconstructing the body's shape or deformations that fits the 3D information.

Abstract: A system and method are presented for estimating the orientation of a panoramic camera mounted on a vehicle relative to the vehicle coordinate frame. An initial pose estimate of the vehicle is determined based on global positioning system data, inertial measurement unit data, and wheel odometry data of the vehicle. Image data from images captured by the camera is processed to obtain one or more tracks, each track including a sequence of matched feature points stemming from a same three-dimensional location. A correction parameter determined from the initial pose estimate and tracks can then be used to correct the orientations of the images captured by the camera. The correction parameter can be optimized by deriving a correction parameter for each of a multitude of distinct subsequences of one or more runs. Statistical analysis can be performed on the determined correction parameters to produce robust estimates.