Google Inc.

Amazon Technologies, Inc.

Keunhong Park

Ricardo Martin-Brualla

Matthew Alun Brown

Christopher Derming Xie

Dilip Kumar

Gerard Guy Medioni

Nishitkumar Ashokkumar Desai

Jayakrishnan Kumar Eledath

Liefeng Bo

Nikhil Chacko

Systems and methods for three-dimensional object category modeling can utilize figure-ground neural radiance fields for unsupervised training and inference. For example, the systems and methods can include a foreground model and a background model that can generate an object output based at least in part on one or more learned embeddings. The foreground model and background model may process position data and view direction data in order to output color data and volume density data for a respective position and view direction. Moreover, the object category model may be trained to generate an object output, which may include an instance interpolation, a view synthesis, or a segmentation.

Figure-ground neural radiance fields for three-dimensional object category modelling

This disclosure describes systems and techniques for detecting certain activity in image data, such as frames of video data. For example, the systems and techniques may create and utilize an activity classifier for detecting and classifying certain human activity in video data of a facility. In some instances, the classifier may be trained to identify, from the video data, certain predefined activity such as a user picking an item from a shelf, a user returning an item to a shelf, a first user passing an item to a second user, or the like. In some instances, the techniques enable activity detection using only video data, rather than in addition to data acquired by other sensors.

Vision-based event detection

Growing community of inventors

Seattle, WA, United States of America

Keunhong Park