Waymo LLC

Google Inc.

Noha Waheed Ahmed Radwan

Chen Wu

Jonathon Shlens

Alper Ayvaci

Ruichi Yu

Yu-Han Chen

Jonathan Tilton Barron

Ricardo Martin Brualla

Alexey Dosovitskiy

Seyed Mohammad Mehdi Sajjadi

Daniel Christopher Duckworth

Provided are systems and methods for synthesizing novel views of complex scenes (e.g., outdoor scenes). In some implementations, the systems and methods can include or use machine-learned models that are capable of learning from unstructured and/or unconstrained collections of imagery such as, for example, 'in the wild' photographs. In particular, example implementations of the present disclosure can learn a volumetric scene density and radiance represented by a machine-learned model such as one or more multilayer perceptrons (MLPs).

View synthesis robust to unconstrained image data

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating object interaction predictions using a neural network. One of the methods includes obtaining a sensor input derived from data generated by one or more sensors that characterizes a scene. The sensor input is provided to an object interaction neural network. The object interaction neural network is configured to process the sensor input to generate a plurality of object interaction outputs. Each respective object interaction output includes main object information and interacting object information. The respective object interaction outputs corresponding to the plurality of regions in the sensor input are received as output of the object interaction neural network.

Interacted object detection neural network

Growing community of inventors

Berlin, Germany

Noha Waheed Ahmed Radwan