Hover, Inc.

Ioannis Pavlidis

Adam J Altman

Shaohui Sun

Vineet Bhatawadekar

William Castillo

Manish Upendran

Sarthak Sahu

Francisco Avila-Beltran

Matthew Boyer

Christopher David Ege

Systems and methods are described for creating three dimensional models of building objects by creating a point cloud from a plurality of input images, defining edges of the building object's surfaces represented by the point cloud, creating simplified geometries of the building object's surfaces and constructing a building model based on the simplified geometries. Input images may include ground, orthographic, or oblique images. The resultant model may be scaled according to correlation with select image types and textured.

Systems and methods for generating three dimensional geometry

A method and related software are disclosed for processing imagery related to three dimensional models. To display new visual data for select portions of images, an image of a physical structure such as a building with a façade is retrieved with an associated three dimensional model for that physical structure according to common geolocation tags. A scaffolding of surfaces composing the three dimensional model is generated and regions of the retrieved image are registered to the surfaces of the scaffolding to create mapped surfaces for the image. New image data such as texture information is received and applied to select mapped surfaces to give the retrieved image the appearance of having the new texture data at the selected mapped surface.

Computer vision database platform for a three-dimensional mapping system

Systems and methods are disclosed for directed image capture of a subject of interest, such as a physical building. Directed image capture can produce higher quality images such as content more centrally located within an image frame (or an associated viewing device or other display), higher quality images have greater value for subsequent uses of captured images such as for information extraction or model reconstruction. Graphical guide(s) overlaid within an image frame can facilitate quality assessments for the content or the image frame itself, such as for pixel distance of the subject of interest to a centroid of the image frame (or an associated viewing device or other display), or the effect of obscuring objects. Quality assessments can further include instructions for improving the quality of the image capture for the content of interest.

Directed image capture

Systems and methods are disclosed for directed image capture of a subject of interest, such as a home. Directed image capture can produce higher quality images such as more centrally located within a display and/or viewfinder of an image capture device, higher quality images have greater value for subsequent uses of captured images such as for information extraction or model reconstruction. Graphical guide(s) facilitate content placement for certain positions and quality assessments for the content of interest can be calculated such as for pixel distance of the content of interest to a centroid of the display or viewfinder, or the effect of obscuring objects. Quality assessments can further include instructions for improving the quality of the image capture for the content of interest.

A system is provided including a database that ingests data from disparate image sources, with a variety of image metadata types and qualities, and manages images geospatially through the creation and continued refinement of camera solutions for each data object included. These camera solutions are calculated and refined by the database as additional data enters the system that could affect the solutions, through a combination of the application of image metadata towards image processing methods and the use of optical-only computer vision techniques. The database continually generates data quality metrics and relevant imagery and geometry analytics, which drive future collection tasking, system analytics, and human quality control requirements.

A system and method is provided for generating textured 3D building models from ground-level imagery. Ground-level images for the sides/corners of building objects are collected for identification of key architectural features, corresponding key fa&#xe7;ade geometry planes, and generation of a 3D building fa&#xe7;ade geometry. The 3D building model is properly geo-positioned, scaled and textured.

Generating multi-dimensional building models with ground level images

Systems and methods are described for adjusting planar geometry derived from point cloud data associated with a building object by identifying major aspects like vertices and edges of the underlying structure within the point cloud and adjusting an average planar fit for simplified geometries created from the point cloud. Combining the planar geometry or point cloud with orthogonal or street level views improves accuracy and use cases of the planar geometry.

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Redwood City, CA, United States of America

Ioannis Pavlidis