The detailed 3D environments can be used, for example, for planning and testing routes.

The project was developed in cooperation with Alphabet’s Waymo company, which specializes in autonomous driving. Google cites training of autonomous vehicles or other robots, as well as support for aerial mapping, as potential applications for block NeRFs. NeRFs could enable Google Maps Street View in 3D However, improvements are already planned, the team said. The rendering in the distance is currently also washed out. Multiple block NeRFs can render in parallel, which is necessary in some scenes where the blocks overlap. However, shadows of vehicles are still visible in some images, and changing vegetation results in washed-out trees and bushes along the roadway. Pedestrians, cars and other changing objects are automatically filtered out by the system during AI training. The different lighting and weather conditions of the recordings enable the NeRF networks to display the road scenes under different conditions as well. The block NeRFs were trained with nearly 2.8 million images captured over three months by camera-equipped cars. The split decouples render time from scene size, allows scaling to environments of any size, and allows per-block updates for changes such as construction sites. This is made possible by a network of multiple NeRFs, each responsible for a separate block in the city. Now, Google is demonstrating an approach with Block-NeRF that can render the largest 3D scene ever rendered with AI: Alamo Square in San Francisco, consisting of eight streets. Until now, however, NeRFs have only been used to render individual objects or rooms.

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Google 3d map view