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Determine specularity from image

A technology of specular reflection and image application provides a further aspect for determining specular reflection and determining the field of specular reflectivity, which can solve problems such as meaningless results, low data processing efficiency, and difficulties

Active Publication Date: 2018-12-04
GOOGLE LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, modeling the specularity of objects from images in non-laboratory settings can be noisy and more difficult
As an example, the number of unknowns can become very large, making the processing of large amounts of data for many different objects inefficient and time-consuming, even for small numbers of images (such as 40 or less)
In some examples, the entire process may break and provide nonsensical results

Method used

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Examples

Experimental program
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Embodiment Construction

[0023] overview

[0024] The technique generally involves determining specular reflectivity using multiple aerial photographs taken from various angles. In one example, determining whether a surface has emissive material includes determining whether a surface patch is brighter when rays reflected from the sun are aligned with rays directed toward the patch from the center of the camera. If the observed intensity of the patch is high around the alignment angle and darker for other angles, this can be an indicator for the luminescent material. All other cases can be assumed to be non-luminescent materials.

[0025] By determining whether an object is shiny (or, for example, the level of specularity of the object), such objects can be rendered in a manner that appears more realistic to the user. For example, if a user is viewing a three-dimensional (3D) model of an actual geographic location, such as a city, the specularity of the object can be used to accentuate certain areas ...

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Abstract

Aspects of the disclosure generally relate to determining specular reflectivity of an object. As an example, a region 512, 712, 812, 1304 of an object or geometric shape may be selected. A collection of images 510 , 520 , or 620 , or 710 , 720 , or 810 , 820 , or 1306 containing the area 512 of the geometric shape may be captured. The set of images may be filtered to remove images of areas that do not show the geometry well, such as if the area is in shadow or occluded by another object. For each image, a set of intensity values ​​for that region is determined. A set of angle values ​​for each image is determined based at least on the orientation of the camera that captured the particular image when the particular image was captured. The set of mean intensities and the set of angle values ​​are paired and fitted to curves 636 , 840 , 1308 . Based at least on that fit, the specularity of the region is then classified.

Description

[0001] Cross References to Related Applications [0002] This application is a continuation of U.S. Patent Application No. 14 / 319,024, filed June 30, 2014, which is U.S. Patent Application No. 14 / 107,210, filed December 16, 2013 (now U.S. Patent No. 8,805,088 patent), the contents of which are incorporated herein by reference. Background technique [0003] Various systems can be used to determine the specularity of an object. Typically, these systems utilize specific lighting and surface conditions in a laboratory environment. Therefore, modeling the specularity of objects from images in non-laboratory settings can be noisy and more difficult. As an example, the unknown number can become very large, making the processing of large amounts of data for many different objects inefficient and time-consuming, even for a small number of images (such as 40 or less). In some examples, the entire process may break and provide meaningless results. Contents of the invention [0004...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T15/50G06T17/05G06T7/90
CPCG06T2207/10032G01N21/55G06T15/50G06T17/05G06T17/00G06T7/507G06T7/90G06T7/62G06K17/00G06V10/60G06V10/40G06V40/172G06T17/10
Inventor A·L·梅索朗吉蒂斯M·A·杜钱恩奥J·琼斯
Owner GOOGLE LLC