Microsoft Technology Licensing, LLC

Mftb Holdco, Inc.

Digital Equipment Corporation

Compaq Computer Corporation, Inc.

Hewlett-packard Development Company, L.p.

Zillow, Inc.

Compaq Information Technologies Group, L.p.

Mfib Holdco, Inc.

Sing Bing Kang

Richard S Szeliski

Matthew Uyttendaele

Charles Lawrence Zitnick, Iii

Simon Winder

Brian Kevin Guenter

Sashi Raghupathy

Kirk O Olynyk

Stephen Marschner

Heung-Yeung Shum

Shahram Izadi

Neel Suresh Joshi

Naji Khosravan

Baining Guo

Andreas Georgiou

Adam G Kirk

Lifeng Wang

Pierre Moulon

Baoyuan Wang

Jian Qiao Sun

Michael F Cohen

Ivaylo Boyadzhiev

Eric Paul Bennett

Christoph Rhemann

Oliver Arthur Whyte

Mitchell David Dawson

Keith Waters

Sean P Cier

Michael Jack Sinclair

Xiaoou Tang

Jamie Daniel Joseph Shotton

Joel Steven Kollin

Denis Demandolx

Steven Maxwell Seitz

Sudipta Narayan Sinha

Simon John Baker

Gary Keith Starkweather

Ce Liu

James Matthew Rehg

Marc Andre Leon Pollefeys

Yasuyuki Matsushita

Yin Li

Frederic Dufaux

Bryan Kent Ressler

Lambert E Wixson

Joshua Bryan Weisberg

Charles L Zitnick

Richard S Weiss

Viktoriya Stoeva

Yujie Li

Pablo Alejandro Speciale

Steven Nabil Bathiche

Steven Mark Drucker

Kalyan Krishna Sunkavalli

Robert Alan Ulichney

Kestutis Patiejunas

Hugues Herve Hoppe

Antonio Criminisi

Pushmeet Kohli

Ying-Qing Xu

Nebojsa Jojic

Ashish Kapoor

Shengyang Dai

Christopher J Buehler

Robert Matthew Craig

Stephen S Lin

David Kim

Vivek Pradeep

Peter-Pike Johannes Sloan

David Eraker

Avronil Bhattacharjee

Yuguang Li

Lu Yuan

Jonathan E Lange

Takaaki Shiratori

Cem Keskin

Michael Rondinelli

Jian Sun

Johannes P Kopf

Luis Eduardo Cabrera-Cordon

Wolf Kienzle

Dax H Hawkins

Pablo Luis Sala

Duncan Paul Robertson

David Sweeney

Will Adrian Hutchcroft

Ben Vaught

Kevin Karsch

Habib Zargarpour

Xinguo Liu

Sean Ryan Francesco Fanello

Liang Wang

Joe Bertolami

Yaron Eshet

Matheen Siddiqui

Benjamin William Ward

Tae-Hyun Oh

Jagannatha Koppal

Juan Caicedo Rueda

Yunpeng Li

A valuation system to identify interior features of a property, identify exterior features related to interior features using aerial images, and to generate property valuations based on the identified features is provided. The valuation system identifies, using a computer vision module, interior features based on interior image data (e.g., photos) of a property, and further identifies exterior features associated with any of the interior features based on an aerial photo of the property. For example, trees and buildings (e.g., exterior features) adjacent to a window (e.g., an interior feature) can be identified by a computer vision module through the combination of interior and exterior image data. In other words, the identification of property features by the computer vision module can be enriched by correlating interior image data (e.g., photos, video walkthroughs) to exterior image data (e.g., satellite photos, aerial photos).

Home valuation using identified exterior and interior features systems and methods

Techniques are described for automated operations to analyze visual data from images acquired in multiple rooms of a building to generate multiple types of building information (e.g., to include a floor plan for the building), such as by simultaneously or otherwise concurrently analyzing groups of three or more images having at least pairwise visual overlap between pairs of those images to determine information that includes global inter-image pose and structural element locations, and for subsequently using the generated building information in one or more further automated manners, with the building information generation further performed in some cases without having or using information from any distance-measuring devices about distances from an image's acquisition location to walls or other objects in the surrounding room.

Automated inter-image analysis of multiple building images for building floor plan generation

Aspects of the subject disclosure are directed towards safely projecting a diffracted light pattern, such as in an infrared laser-based projection/illumination system. Non-diffracted (zero-order) light is refracted once to diffuse (defocus) the non-diffracted light to an eye safe level. Diffracted (non-zero-order) light is aberrated twice, e.g., once as part of diffraction by a diffracting optical element encoded with a Fresnel lens (which does not aberrate the non-diffracted light), and another time to cancel out the other aberration; the two aberrations may occur in either order. Various alternatives include upstream and downstream positioning of the diffracting optical element relative to a refractive optical element, and/or refraction via positive and negative lenses.

Diffractive optical element with undiffracted light expansion for eye safe operation

Techniques are described for automated operations related to analyzing visual data from images captured in rooms of a building and optionally additional captured data about the rooms to assess room layout and other usability information for the building's rooms and optionally for the overall building, and to subsequently using the assessed usability information in one or more further automated manners, such as to improve navigation of the building. The automated operations may include identifying one or more objects in each of the rooms to assess, evaluating one or more target attributes of each object, assessing usability of each object using its target attributes' evaluations and each room using its objects' assessment and other room information with respect to an indicated purpose, and combining the assessments of multiple rooms in a building and other building information to assess usability of the building with respect to its indicated purpose.

Automated usability assessment of buildings using visual data of captured in-room images

Techniques are described for using computing devices to perform automated operations to generate mapping information of a defined area via analysis of visual data of images, including by using attribute information exchanged between paired or otherwise grouped images of multiple types to generate enhanced images, and for using the generated mapping information in further automated manners, including to use the generated mapping information for automated navigation and/or to display or otherwise present the generated mapping information. In some situations, the defined area includes an interior of a multi-room building, and the generated information includes at least one or more enhanced images and/or a partial floor plan and/or other modeled representation of the building, with the generating performed in some cases without having measured depth information about distances from the images' acquisition locations to walls or other objects in the surrounding building.

Automated exchange and use of attribute information between building images of multiple types

Techniques are described for using computing devices to perform automated operations for determining matching buildings and using corresponding information in further automated manners, such as by determining buildings having similarities to other indicated buildings based at least in part on building floor plans and/or other attributes, and by automatically generating descriptions of the similarities. In some situations, such matching building determinations are based on generating and using adjacency graphs for floor plans that represent inter-connections between rooms and other attributes of the buildings, and further generating and comparing vector-based embeddings that concisely represent the information of the adjacency graphs for use in performing inter-building comparisons and generating the descriptions. Information about such determined buildings may be used in various automated manners, including for controlling device navigation (e.g., autonomous vehicles), for display on client devices in corresponding graphical user interfaces, for further analysis to identify shared and/or aggregate characteristics, etc.

Automated building identification using floor plans and acquired building images

Techniques are described for using multiple devices to automatically determine the acquisition location of an image from a camera device, such as within a building interior and by using data from the multiple devices (e.g., visual data from the camera device's image, and additional data captured by a separate mobile computing device in the same area as the camera device), and for subsequently using determined image acquisition location information in one or more further automated manners. The image may be a panorama image or of another type, and the determined acquisition location for such an image may be at least a location on the building's floor plan&#x2014;in addition, the automated image acquisition location determination may be further performed without having or using information from any depth sensors or other distance-measuring devices about distances from an image's acquisition location to walls or other objects in the surrounding building.

Automated determination of image acquisition locations in building interiors using multiple data capture devices

Techniques are described for using a smart phone or other mobile device to perform automated operations for generating panorama images of building environments and for subsequently using the generated panorama images in further automated manners. In at least some situations, the generation of a panorama image by a mobile device is based at least in part on automatically acquiring multiple constituent images on the mobile device in multiple directions from an acquisition location and on concurrently combining acquired constituent images on the mobile device, such as in a real-time manner relative to the constituent image capture, to generate a panorama image with 360&#xb0; of horizontal coverage of the view from that acquisition location. Information about such identified building floor plans may be used in various automated manners, including for controlling navigation of devices (e.g., autonomous vehicles), for display on client devices in corresponding graphical user interfaces, etc.

Automated generation on mobile devices of panorama images for building locations and subsequent use

The subject disclosure is directed towards controlling the intensity of illumination of a scene or part of a scene, including to conserve illumination power. Quality of depth data in stereo images may be measured with different illumination states; environmental conditions, such as ambient light, natural texture may affect the quality. The illumination intensity may be controllably varied to obtain sufficient quality while conserving power. The control may be directed to one or more regions of interest corresponding to an entire scene or part of a scene.

Environment-dependent active illumination for stereo matching

An 'Adaptive Exposure Corrector' performs automated real-time exposure correction of individual images or image sequences of arbitrary length. 'Exposure correction' is defined herein as automated adjustments or corrections to any combination of shadows, highlights, high-frequency features, and color saturation of images. The Adaptive Exposure Corrector outputs perceptually improved images based on image ISO and camera ISO capabilities in combination with camera noise characteristics via exposure corrections by a variety of noise-aware image processing functions. An initial calibration process adapts these noise aware image processing functions to noise characteristics of particular camera models and types in combination with particular camera ISO settings. More specifically, this calibration process precomputes a Noise Aware Scaling Function (NASF) and a Color Scalar Function (CSF). The NASF and CSF are then applied to adapt various image processing functions that are subsequently applied to perform real-time noise-aware exposure corrections on images as those images are being captured.

Real-time adaptive shadow and highlight enhancement

Computing devices and methods for estimating a pose of a user computing device are provided. In one example a 3D map comprising a plurality of 3D points representing a physical environment is obtained. Each 3D point is transformed into a 3D line that passes through the point to generate a 3D line cloud. A query image of the environment captured by a user computing device is received, the query image comprising query features that correspond to the environment. Using the 3D line cloud and the query features, a pose of the user computing device with respect to the environment is estimated.

Device pose estimation using 3D line clouds

Systems and methods for stereo matching based upon active illumination using a patch in a non-actively illuminated image to obtain weights that are used in patch similarity determinations in actively illuminated stereo images is provided. To correlate pixels in actively illuminated stereo images, adaptive support weights computations are used to determine similarity of patches corresponding to the pixels. In order to obtain adaptive support weights for the adaptive support weights computations, weights are obtained by processing a non-actively illuminated ('clean') image.

Active stereo with adaptive support weights from a separate image

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Redmond, WA, United States of America

Sing Bing Kang