10 DAILY CVPR Thursday NeuRAD integrates a variety of stateof-the-art methods across different NeRF techniques. “Previously, people have been focused very much on the camera-only setting and not that much on automotive data,” Carl points out. “The techniques focused on automotive data have not included a full sensor setup with 360-degree cameras and lidar. We focus on trying to capture the full sensor setup commonly used in AD.” However, the road to developing NeuRAD has been challenging, involving navigating a wide range of existing ideas and methods. The team wanted to bring out the best of all the different methods without creating a convoluted system. “We had to cut a clear, narrow path that makes a clean method while including some of the most clever advances out there and adding our own flavor on top,” Adam tells us. “Navigating that jungle of ideas was difficult.” Computer vision is at the heart of this method, particularly in its advanced scene representation. The team aims to render vision data, including images and lidar points clouds, by learning a 3D representation of the actual world from collected data. Constructing that 3D representation is a big challenge but critical, as it allows for efficient rendering of the visual information necessary for AD systems. NeuRAD’s practical applications are vast. “One that’s very useful for us here at Zenseact is the ability to collect data from driving in a normal Highlight Presentation “Navigating that jungle of ideas was difficult!”
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