Google Inc.

Michael Hongmai Lin

Tilman Reinhardt

Joshua Sam Schpok

Aleksey Golovinskiy

David Robert Gallup

Brett Allen

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.

Bundle adjustment based on image capture intervals

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.

Feature reduction based on local densities for bundle adjustment of images

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.

Systems, methods and computer apparatuses for computing three dimensional (3D) surfaces are described herein. An embodiment includes constructing a slanted voxel grid oriented in a direction between at least two camera locations, projecting images from respective camera locations onto the constructed voxel grid, calculating, for one or more voxels in the voxel grid, a magnitude of difference between pixels corresponding to respective projected images to provide a difference value for each of the one or more voxels, and computing a three dimensional surface passing through voxel locations corresponding to one or more calculated difference values of the one or more voxels. In an embodiment, the three dimensional surface is computed as a surface passing through voxel locations corresponding to calculated difference values having lower magnitudes with respect to other calculated difference values.

Methods and systems to compute 3D surfaces

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.

Combining narrow-baseline and wide-baseline stereo for three-dimensional modeling

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.

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Mountain View, CA, United States of America

Michael Hongmai Lin