Microsoft Technology Licensing, LLC

Trebor Lee Connell

Michael Ebstyne

Emanuel Shalev

Pedro Urbina Escos

Jonathan C Hanzelka

Larry Marvin Wall

Don Dongwoo Kim

Alvin Wen Jie Chia

Systems and methods are disclosed for leveraging rendering engines to perform multi-spectral rendering by reusing the color channels for additional spectral bands. A digital asset represented by a three dimensional (3D) mesh and a material reference pointer may be rendered using a first material spectral band data set and additionally rendered using a second material spectral band data set, and the results combined to create a multi-spectral rendering. The multi-spectral rendering may then be used as part of a synthetics service or operation. By abstracting the material properties, a material translator is able to return a banded material data set from among a plurality of spectral band sets, and asset material information may advantageously be managed apart from managing each asset individually.

Multi-spectral rendering for synthetics

Data representing a scene is received. The scene includes labeled elements such as walls, a floor, a ceiling, and objects placed at various locations in the scene. The original received scene may be modified in different ways to create new scenes that are based on the original scene. These modifications include adding clutter to the scene, moving one or more elements of the scene, swapping one or more elements of the scene with different labeled elements, changing the size, color, or materials associated with one or more of the elements of the scene, and changing the lighting used in the scene. Each new scene may be used to generate labeled training data for a classifier by placing a virtual sensor (e.g., a camera) in the new scene, and generating sensor output data for the virtual sensor based on its placement in the new scene.

Generating training data for machine learning classifier

Systems and methods are disclosed for permitting the use of a natural language expression to specify object (or asset) locations in a virtual three-dimensional (3D) environment. By rapidly identifying and solving constraints for 3D object placement and orientation, consumers of synthetics services may more efficiently generate experiments for use in development of artificial intelligence (AI) algorithms and sensor platforms. Parsing descriptive location specifications, sampling the volumetric space, and solving pose constraints for location and orientation, can produce large numbers of designated coordinates for object locations in virtual environments with reduced demands on user involvement. Converting from location designations that are natural to humans, such as 'standing on the floor one meter from a wall, facing the center of the room' to a six-dimensional (6D) pose specification (including 3-D location and orientation) can alleviate the need for a manual drag/drop/reorient procedure for placement of objects in a synthetic environment.

Placing and solving constraints on a 3D environment

Examples are disclosed that relate to computing devices and methods for simulating light passing through one or more lenses. In one example, a method comprises obtaining a point spread function of the one or more lenses, obtaining a first input raster image comprising a plurality of pixels, and ray tracing the first input raster image using the point spread function to generate a first output image. Based on ray tracing the first input raster image, a look up table is generated by computing a contribution to a pixel in the first output image, wherein the contribution is from a pixel at each location of a subset of locations in the first input raster image. A second input raster image is obtained, and the look up table is used to generate a second output image from the second input raster image.

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Seattle, WA, United States of America

Trebor Lee Connell