Virtual World Generator: Difference between revisions
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Client: Andy Fawkes, [[Bohemia Interactive Simulations]] <andy.fawkes@bisimulations.com> | Client: Andy Fawkes, [[Bohemia Interactive Simulations]] <andy.fawkes@bisimulations.com> | ||
It is possible to make accurate 3D scans of indoor scenes using depth cameras such as Google Tango or expensive LIDAR scanners. Although the overall geometry is accurate, individual objects cannot be distinguished.Your task is to use a simple SLAM algorithm to recover overall room dimensions, but populate a navigable virtual world in OpenGL using standard 3D library models of furniture and other objects that have been recognised as belonging to relevant categories using pre-trained deep neural net models such as NeuralTalk Model Zoo. | It is possible to make accurate 3D scans of indoor scenes using depth cameras such as Google Tango or expensive LIDAR scanners. Although the overall geometry is accurate, individual objects cannot be distinguished. Your task is to use a simple SLAM algorithm to recover overall room dimensions, but populate a navigable virtual world in OpenGL using standard 3D library models of furniture and other objects that have been recognised as belonging to relevant categories using pre-trained deep neural net models such as NeuralTalk Model Zoo. | ||
Latest revision as of 16:45, 7 November 2017
Client: Andy Fawkes, Bohemia Interactive Simulations <andy.fawkes@bisimulations.com>
It is possible to make accurate 3D scans of indoor scenes using depth cameras such as Google Tango or expensive LIDAR scanners. Although the overall geometry is accurate, individual objects cannot be distinguished. Your task is to use a simple SLAM algorithm to recover overall room dimensions, but populate a navigable virtual world in OpenGL using standard 3D library models of furniture and other objects that have been recognised as belonging to relevant categories using pre-trained deep neural net models such as NeuralTalk Model Zoo.
