Abstract: 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.

Abstract: The disclosure herein describes training a machine learning model to recognize a real-world object based on generated virtual scene variations associated with a model of the real-world object. A digitized three-dimensional (3D) model representing the real-world object is obtained and a virtual scene is built around the 3D model. A plurality of virtual scene variations is generated by varying one or more characteristics. Each virtual scene variation is generated to include a label identifying the 3D model in the virtual scene variation. A machine learning model may be trained based on the plurality of virtual scene variations. The use of generated digital assets to train the machine learning model greatly decreases the time and cost requirements of creating training assets and provides training quality benefits based on the quantity and quality of variations that may be generated, as well as the completeness of information included in each generated digital asset.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The disclosure herein describes training a machine learning model to recognize a real-world object based on generated virtual scene variations associated with a model of the real-world object. A digitized three-dimensional (3D) model representing the real-world object is obtained and a virtual scene is built around the 3D model. A plurality of virtual scene variations is generated by varying one or more characteristics. Each virtual scene variation is generated to include a label identifying the 3D model in the virtual scene variation. A machine learning model may be trained based on the plurality of virtual scene variations. The use of generated digital assets to train the machine learning model greatly decreases the time and cost requirements of creating training assets and provides training quality benefits based on the quantity and quality of variations that may be generated, as well as the completeness of information included in each generated digital asset.

Abstract: 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.

Abstract: A data storage management system for an enterprise data storage system is provided for aggregating unused data storage space as a contiguous standardized data storage space on a distributed network system.