Abstract: Methods, systems, and computer program products are provided for determining camera parameters and three dimensional locations of features from a plurality of images of a geographic area. These include, determining a correlation between a pose of a first camera and a pose of a second camera, generating one or more constraints incorporating the correlation, and determining at least one of camera parameters and three dimensional locations of features using a plurality of constraints including the generated one or more constraints. The first camera and the second camera have substantially rigid positions and poses relative to each other. A strength of the correlation is based at least upon a time interval between respective image captures by the first camera and the second camera.

Abstract: Methods, systems, and computer program products are provided for determining camera parameters and three dimensional locations of features from a plurality of images of a geographic area. These include, detecting features in the plurality of images where each of the images cover at least a portion of the geographic area, comparing the detected features between respective ones of the images to determine a plurality of matched features, selecting a subset of the plurality of matched features, and determining the camera parameters and the three dimensional positions of one or more of the detected features using the selected subset. The respective matched features are selected depending on a quantity of other matched features in proximity to the respective matched features.

Abstract: Methods, systems, and computer program products are provided for determining camera parameters and three dimensional locations of features from a plurality of images of a geographic area. These include, detecting features in the plurality of images where each of the images cover at least a portion of the geographic area, comparing the detected features between respective ones of the images to determine a plurality of matched features, selecting a subset of the plurality of matched features and determining the camera parameters and the three dimensional positions of one or more of the detected features using the selected subset. The respective matched features are selected depending on a quantity of other matched features in proximity to the respective matched features.

Abstract: Methods, systems, and computer program products are provided for determining camera parameters and three dimensional locations of features from a plurality of images of a geographic area. These include, determining a correlation between a pose of a first camera and a pose of a second camera, generating one or more constraints incorporating the correlation, and determining at least one of camera parameters and three dimensional locations of features using a plurality of constraints including the generated one or more constraints. The first camera and the second camera have substantially rigid positions and poses relative to each other. A strength of the correlation is based at least upon a time interval between respective image captures by the first camera and the second camera.

Abstract: The subject matter of this specification can be implemented in, among other things, a method for generating a set of planar mediums (e.g., pages) for three dimensional model construction. The method includes a step for receiving three dimensional object data from a client computing device. The method includes a step for categorizing the three dimensional object data into a plurality of faces, wherein each face represents a two dimensional polygon of a part of the three dimensional object, and wherein connectivity information for each face of the plurality of faces to other faces of the plurality of faces is maintained. The method includes a step for selecting two faces from the plurality of faces for digital joining. The method includes a step for digitally joining the selected two faces to fit on a planar medium. The method includes a step for generating one or more digital images which contain the digitally joined faces from the plurality of faces.

Abstract: Methods, systems, and computer program products are provided for determining camera parameters and three dimensional locations of features from a plurality of images of a geographic area. These include, determining a correlation between a pose of a first camera and a pose of a second camera, generating one or more constraints incorporating the correlation, and determining at least one of camera parameters and three dimensional locations of features using a plurality of constraints including the generated one or more constraints. The first camera and the second camera have substantially rigid positions and poses relative to each other. A strength of the correlation is based at least upon a time interval between respective image captures by the first camera and the second camera.

Abstract: Methods, systems, and computer program products are provided for determining camera parameters and three dimensional locations of features from a plurality of images of a geographic area. These include, detecting features in the plurality of images where each of the images cover at least a portion of the geographic area, comparing the detected features between respective ones of the images to determine a plurality of matched features, selecting a subset of the plurality of matched features, and determining the camera parameters and the three dimensional positions of one or more of the detected features using the selected subset. The respective matched features are selected depending on a quantity of other matched features in proximity to the respective matched features.

Abstract: Methods, systems, and computer program products are provided for determining camera parameters and three dimensional locations of features from a plurality of images of a geographic area. These include, determining a correlation between a pose of a first camera and a pose of a second camera, generating one or more constraints incorporating the correlation, and determining at least one of camera parameters and three dimensional locations of features using a plurality of constraints including the generated one or more constraints. The first camera and the second camera have substantially rigid positions and poses relative to each other. A strength of the correlation is based at least upon a time interval between respective image captures by the first camera and the second camera.

Abstract: In an embodiment, a method merges a plurality of three-dimensional models, each having been generated from images of a camera having a different viewpoint. For respective voxels in a plurality of voxels, a plurality of distance values are determined. Each determined distance value is a distance from the voxel to a three dimensional model from the plurality of three-dimensional models along a perspective of a camera model used to generate the three-dimensional model. When at least one of the determined distance values indicates that the distance between the voxel and the three dimensional model exceeds a threshold and the voxel is located above the corresponding three-dimensional model, a confidence score is determined indicating a degree to which the perspective of the camera model of the corresponding three-dimensional model is oriented to face the voxel. Based on the determined confidence score, the voxel into a merged three-dimensional model is determined.

Abstract: In an embodiment, a method determines a three-dimensional model from a plurality of images taken of a geographic region by one or more cameras from different perspectives. The method includes determining, using a first stereo reconstruction technique: (i) a plurality of three-dimensional candidate surface points from the plurality of images, and (ii) which of the images in the plurality of images view each of the plurality of candidate surface points. The method also includes identifying an empty space between each of the plurality of candidate surface points and each camera model for the respective images determined to view the candidate surface point. The method further includes for each of a plurality of pairs of images from the plurality of images, determining, using a second stereo reconstruction technique, a surface estimate for the pair of images. The method also includes merging the surface estimates to identify a final surface.

Abstract: Dense range data obtained at real-time rates is employed to estimate the pose of an articulated figure. In one approach, the range data is used in combination with a model of connected patches. Each patch is the planar convex hull of two circles, and a recursive procedure is carried out to determine an estimate of pose which most closely correlates to the range data. In another aspect of the invention, the dense range data is used in conjunction with image intensity information to improve pose tracking performance. The range information is used to determine the shape of an object, rather than assume a generic model or estimate structure from motion. In this aspect of the invention, a depth constraint equation, which is a counterpart to the classic brightness change constraint equation, is employed. Both constraints are used to jointly solve for motion estimates.

Abstract: Dense range data obtained at real-time rates is employed to estimate the pose of an articulated figure. In one approach, the range data is used in combination with a model of connected patches. Each patch is the planar convex hull of two circles, and a recursive procedure is carried out to determine an estimate of pose which most closely correlates to the range data. In another aspect of the invention, the dense range data is used in conjunction with image intensity information to improve pose tracking performance. The range information is used to determine the shape of an object, rather than assume a generic model or estimate structure from motion. In this aspect of the invention, a depth constraint equation, which is a counterpart to the classic brightness change constraint equation, is employed. Both constraints are used to jointly solve for motion estimates.