Abstract: Described is a computer-implemented method which comprises receiving a plurality of images captured by at least one user device, wherein each image is associated with one of a corresponding plurality of geographic locations; determining a path between the plurality of geographic locations; determining a confidence indicator representative of whether the determined path corresponds to a demarked path, wherein determining the confidence indicator comprises determining a time of capture of each of the plurality of images; identifying the path as corresponding to a demarked route, based on the confidence indicator; and marking the plurality of images for display as a demarked route.

Abstract: Described is a computer-implemented method which comprises receiving a plurality of images captured by at least one user device, wherein each image is associated with one of a corresponding plurality of geographic locations; determining a path between the plurality of geographic locations; determining a confidence indicator representative of whether the determined path corresponds to a demarked path, wherein determining the confidence indicator comprises determining a time of capture of each of the plurality of images; identifying the path as corresponding to a demarked route, based on the confidence indicator; and marking the plurality of images for display as a demarked route.

Abstract: Described is a computer-implemented method which comprises receiving a plurality of images captured by at least one user device, wherein each image is associated with one of a corresponding plurality of geographic locations; determining a path between the plurality of geographic locations; determining a confidence indicator representative of whether the determined path corresponds to a demarked path, wherein determining the confidence indicator comprises determining a time of capture of each of the plurality of images; identifying the path as corresponding to a demarked route, based on the confidence indicator; and marking the plurality of images for display as a demarked route.

Abstract: Described is a computer-implemented method which comprises receiving a plurality of images captured by at least one user device, wherein each image is associated with one of a corresponding plurality of geographic locations; determining a path between the plurality of geographic locations; determining a confidence indicator representative of whether the determined path corresponds to a demarked path, wherein determining the confidence indicator comprises determining a time of capture of each of the plurality of images; identifying the path as corresponding to a demarked route, based on the confidence indicator; and marking the plurality of images for display as a demarked route.

Abstract: Described is a computer-implemented method which comprises receiving a plurality of images captured by at least one user device (120), wherein each image is associated with one of a corresponding plurality of geographic locations (A-F, L-R); determining a path between the plurality of geographic locations (201, 202); determining a confidence indicator (1050, 1150) representative of whether the determined path corresponds to a demarked path, wherein determining the confidence indicator comprises determining a time of capture of each of the plurality of images (601, 602); identifying the path as corresponding to a demarked route (1231), based on the confidence indicator; and marking the plurality of images for display as a demarked route.

Abstract: This technology relates to optimizing location and orientation information of an image using known locations of places captured within the image. For example, an image and associated pose data including the image's orientation and location may be received. One or more places captured within the image may be determined, with each place having a respective known location. The image may be annotated with the one or more places. A difference between each annotation and its respective known location to obtain updated pose data of the image may be minimized and the associated pose data may be updated to the updated pose data.

Abstract: This technology relates to optimizing location and orientation information of an image using known locations of places captured within the image. For example, an image and associated pose data including the image's orientation and location may be received. One or more places captured within the image may be determined, with each place having a respective known location. The image may be annotated with the one or more places. A difference between each annotation and its respective known location to obtain updated pose data of the image may be minimized and the associated pose data may be updated to the updated pose data.

Abstract: Systems and methods for processing textures to be applied to surface of a three-dimensional model, such as a three-dimensional model of a geographic area, are provided. According to aspects of the present disclosure, a two-dimensional image processing operation can be performed in the two-dimensional texture atlas space defined by a texture atlas using manifold neighborhoods defined for pixels in the texture atlas. The manifold neighborhood for a pixel can be the set of texture atlas pixels whose corresponding position on the surface of the three-dimensional model lies within a threshold distance of the surface position corresponding to the pixel in the three-dimensional model. The two-dimensional image processing operations can be performed using the set of texture atlas pixels in the manifold neighborhood.

Abstract: Systems and methods for generating a composite image from a plurality of source images using a scene dependent multi-band blending operation are provided. The multi-band blending operation implements a filtering operation to reduce blending between objects or surfaces that have natural color and/or brightness differences. More particularly, the typical space invariant upsampling that occurs during multi-band blending can be replaced by a scene dependent filtering operation during upsampling that omits or reduces contributions from pixels associated with different objects in a scene during the multi-band blending process. The scene dependent filtering can be based on scene dependent data, such as height data or slope data, which can be used to identify different objects in a scene.

Abstract: The aspects described herein relate to replacing pixels in images in order to remove obstructions from images. In one example, an image of a scene having symmetrical features and image information identifying at least one pixel of the image corresponding to a hole to be filled may be received. This hole may correspond to an obstruction in the image. A set of symmetry axes may be identified based on the symmetrical features. A symmetry map identifying correspondences between different pixels in the image based on the set of symmetry axes may be generated. A correspondence between the at least one pixel corresponding to a hole to be filled and a second pixel of the image is identified based at least in part on the symmetry map and the image information. The at least one pixel may be altered based on the identified correspondence in order to remove the obstruction.

Abstract: An example method and system for blending textures of a composite image formed by a plurality of source images mapped onto a three dimensional model are presented. The composite image is projected to obtain an unblended projected image having textures. The textures are blended to obtain a blended projected image. Both the unblended and the blended projected images are backprojected onto the three dimensional model. A difference is determined between a pixel of the backprojected, blended image and a corresponding pixel of the backprojected, unblended image. The determined difference is then applied to a further corresponding pixel of the composite image to obtain a modified composite image representing a blending of the plurality of source images in the composite image.

Abstract: An exemplary method for simplifying a texture of a three-dimensional model includes simplifying a first three-dimensional model to determine a second three-dimensional model. The first three-dimensional model has a higher resolution than the second three-dimensional model. The method also includes allocating a texture atlas for the second three-dimensional model. The method further includes filling in the texture atlas for the second three-dimensional model.

Abstract: An example method and system for blending textures of a composite image formed by a plurality of source images mapped onto a three dimensional model are presented. The composite image is projected to obtain an unblended projected image having textures. The textures are blended to obtain a blended projected image. Both the unblended and the blended projected images are backprojected onto the three dimensional model. A difference is determined between a pixel of the backprojected, blended image and a corresponding pixel of the backprojected, unblended image. The determined difference is then applied to a further corresponding pixel of the composite image to obtain a modified composite image representing a blending of the plurality of source images in the composite image.

Abstract: An exemplary method for simplifying a texture of a three-dimensional model includes simplifying a first three-dimensional model to determine a second three-dimensional model. The first three-dimensional model has a higher resolution than the second three-dimensional model. The method also includes allocating a texture atlas for the second three-dimensional model. The method further includes filling in the texture atlas for the second three-dimensional model.

Abstract: An exemplary method for simplifying the geometry of a three-dimensional mesh includes dividing a first three-dimensional mesh into a first plurality of tiles along initial boundary lines. The method also includes for at least one tile of the first plurality of tiles, reducing the vertices toward a center of the tile while maintaining vertices along a perimeter of the tile, and assembling the first plurality of tiles to determine a second three-dimensional mesh. The method also includes dividing the second three-dimensional mesh into a second plurality of tiles along boundary lines offset from the initial boundary lines. The method further includes for at least one tile of the second plurality of tiles, reducing the vertices toward a center of the tile while maintaining vertices along a perimeter of the tile, and assembling the second plurality of tiles to determine a simplified three-dimensional mesh.

Abstract: Systems and methods for generating a composite image from a plurality of source images using a scene dependent multi-band blending operation are provided. The multi-band blending operation implements a filtering operation to reduce blending between objects or surfaces that have natural color and/or brightness differences. More particularly, the typical space invariant upsampling that occurs during multi-band blending can be replaced by a scene dependent filtering operation during upsampling that omits or reduces contributions from pixels associated with different objects in a scene during the multi-band blending process. The scene dependent filtering can be based on scene dependent data, such as height data or slope data, which can be used to identify different objects in a scene.

Abstract: System, methods and computer program product embodiments for roof generation of 3D models from ground acquired data. An embodiment includes grouping a plurality of 3D model facades and aligning each group of facades with aerial imagery. The embodiment further comprises identifying if a space between the facades is located within or outside a 3D model, generating a roof that connects plurality of facades and texturing the generated roof with the aerial imagery. In this way, when 3D data is limited to 3D model facades and lacks roof (and vertical face) information, embodiments of the invention create complete 3D models by generating a roof that connects plurality of facades and texturing the generated roof with the aerial imagery. This greatly improves user experience as 3D models closely approximate their respective real world counterparts.

Abstract: An obstacle detection and tracking system identifies objects in the path of a vehicle equipped with the system and issues a visual, audible, and/or control system warning. The system includes a depth imaging system that acquires depth data from objects in the field of view of a detection zone encompassing at least a portion of the road to be driven upon. It is assumed most of the acquired data represents road plane information. Statistical analysis of the depth image data identifies in (X,Y,Z) space at least one plane of the road being driven, after which identification threshold normal heights above and below the road plane are defined. Imaged objects within the detection zone that are higher or lower than a threshold normal are deemed of potential concern and will generate a warning to the vehicle operator or vehicle.

Abstract: A method and apparatus for automatically generating a three-dimensional computer model from a “point cloud” of a scene produced by a laser radar (LIDAR) system. Given a point cloud of an indoor or outdoor scene, the method extracts certain structures from the imaged scene, i.e., ceiling, floor, furniture, rooftops, ground, and the like, and models these structures with planes and/or prismatic structures to achieve a three-dimensional computer model of the scene. The method may then add photographic and/or synthetic texturing to the model to achieve a realistic model.