Abstract:
Subject matter regards generating a 3D point cloud and registering the 3D point cloud to the surface of the Earth (sometimes called “geo-locating”). A method can include capturing, by unmanned vehicles (UVs), image data representative of respective overlapping subsections of the object, registering the overlapping subsections to each other, and geo-locating the registered overlapping subsections.
Abstract:
A system receives digital images of a geographic location, associates each digital image with ground control points in a set of reference stereo images, and associates each digital image to each other digital image via image to image tiepoints. The system updates a geometry of each image via a bundle adjustment, and uses a prioritized stacking order to establish piecewise linear seam lines between each of the images. The system finally builds a prioritized map in a mosaic space specifying the source image pixels that are used in each region of the output mosaic, and forms the mosaic image using the prioritized map.
Abstract:
A system and method for displaying a three-dimensional surface along with ellipsoids representing covariances. In one embodiment, at a point on a three dimensional surface, an ellipsoid is formed having principal axes proportional to the eigenvalues of a covariance matrix. The ellipsoid and the three-dimensional surface are projected onto a two-dimensional plane for display on a two-dimensional screen to a user. The covariance matrix may be an estimated error covariance or a sample covariance.
Abstract:
A system and methods can create a synthetic image of a target from a 3D data set, by using an electro-optical (EO) image and sun geometry associated with the EO image. In some examples, a 3D surface model is created from a 3D data set. The 3D surface model establishes a local surface orientation at each point in the 3D data set. A surface shaded relief (SSR) is produced from the local surface orientation, from an EO image, and from sun geometry associated with the EO image. Points in the SSR that are in shadows are shaded appropriately. The SSR is projected into the image plane of the EO image. Edge-based registration extracts tie points from the projected SSR. The 3D data set converts the tie points to ground control points. A geometric bundle adjustment aligns the EO image geometry to the 3D data set.
Abstract:
A system and method for displaying a three-dimensional surface along with ellipsoids representing covariances. In one embodiment, at a point on a three dimensional surface, an ellipsoid is formed having principal axes proportional to the eigenvalues of a covariance matrix. The ellipsoid and the three-dimensional surface are projected onto a two-dimensional plane for display on a two-dimensional screen to a user. The covariance matrix may be an estimated error covariance or a sample covariance.
Abstract:
Devices, systems, and methods for three-dimensional (3D) evaluation point (3DEP) identification; wherein a method can include receiving a first conjugate point of a first real two-dimensional (2D) image, receiving a second conjugate point of a second real 2D image, the first and second conjugate points corresponding to a same geographical location, determining a first set of points of a 3D point set that project to within a specified distance of the first conjugate point in the first real 2D image, determining a second set of points of the 3D point set that project to within the specified distance of the second conjugate point in the second real 2D image, identifying a common point in both the first set of points and the second set of points that satisfies a specified heuristic relative to all other points in both the first set of points and the second set of points, and using the point as the 3DEP.
Abstract:
Systems, devices, methods, and computer-readable media for determining planarity in a 3D data set are provided. A method can include receiving or retrieving three-dimensional (3D) data of a geographical region, dividing the 3D data into first contiguous regions of specified first geographical dimensions, determining, for each first contiguous region of the first contiguous regions, respective measures of variation, identifying, based on the respective measures of variation, a search radius, dividing the 3D data into respective second contiguous or overlapping regions with dimensions the size of the identified search radius, and determining, based on the identified search radius, a planarity of each of the respective second contiguous or overlapping regions.
Abstract:
A method can include identifying a geolocation of an object in an image, the method comprising receiving data indicating a pixel coordinate of the image selected by a user, identifying a data point in a targetable three-dimensional (3D) data set corresponding to the selected pixel coordinate, and providing a 3D location of the identified data point.
Abstract:
Discussed herein are devices, systems, and methods for multi-image ground control point (GCP) determination. A method can include extracting, from a first image including image data of a first geographical region, a first image template, the first image template including a contiguous subset of pixels from the first image and a first pixel of the first image indicated by the GCP, predicting a first pixel location of the GCP in a second image, the second image including image data of a second geographical overlapping with the first geographical region, extracting, from the second image, a second image template, the second image template including a contiguous subset of pixels from the second image and a second pixel corresponding to the pixel location, identifying a second pixel of the second image corresponding to a highest correlation score, and adding a second pixel location of the identified pixel to the GCP.
Abstract:
Discussed herein are devices, systems, and methods for multi-image ground control point (GCP) determination. A method can include extracting, from a first image including image data of a first geographical region, a first image template, the first image template including a contiguous subset of pixels from the first image and a first pixel of the first image indicated by the GCP, predicting a first pixel location of the GCP in a second image, the second image including image data of a second geographical overlapping with the first geographical region, extracting, from the second image, a second image template, the second image template including a contiguous subset of pixels from the second image and a second pixel corresponding to the pixel location, identifying a second pixel of the second image corresponding to a highest correlation score, and adding a second pixel location of the identified pixel to the GCP.