Researchers in the Murmann Mixed Signal Group have developed a pipelined chip architecture with inverted residual and linear bottlenecks-based networks for energy efficient Machine Learning inference on edge devices.
Stanford researchers have developed a method called KleinPAT, for creating sound models in seconds, making it cost effective to simulate sounds for many different objects in a virtual environment.
Engineers in the Solgaard lab have developed a high-speed, random access grating light valve (GLV) for phase modulation to steer and focus light in LIDAR and 3D imaging applications.
Stanford researchers have developed a method that allows for 3D semantic parsing of indoor spaces. It receives a 3D point cloud input which is parsed into individual spaces and specific components, such as structural and furniture.
Stanford researchers have patented a data-driven method for building a human shape model that spans variation in both subject shape and pose. The method is based on a representation that incorporates both articulated and non-rigid deformations.
Stanford researchers have patented an automated method for generating articulated human models consisting of both morphological and kinematic model data.
This portfolio of inventions provides the tools for an advanced navigational system and panoramic virtual tours – technology that is incorporated in Google Street View.
Researchers from Stanford University and the Max Planck Institute have patented a new marker-less approach to capturing human performances from multi-view video.
Stanford researchers have designed an intelligent software system that assists users by suggesting furniture arrangements that are based on interior design guidelines.