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Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D

research.nvidia.com · 718 words · saved by 1 readers

The goal of perception for autonomous vehicles is to extract semantic representations from multiple sensors and fuse these representations into a single “bird’s-eye-view” coordinate frame for consumption by motion planning. We propose a new end-to-end architecture that directly extracts a bird’s-eye-view representation of a scene given image data from an arbitrary number of cameras. The core idea behind our approach is to “lift” each image individually into a frustum of features for each camera, then “splat” all frustums into a rasterized bird’s-eye- view grid. By training on the entire camera rig, we provide evidence that our model is able to learn not only how to represent images but how to fuse predictions from all cameras into a single cohesive representation of the scene while being robust to calibration error. On standard bird’s- eye-view tasks such as object segmentation and map segmentation, our model outperforms all baselines and prior work. In pursuit of the goal of learning

Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D Toronto AI Lab Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D Jonah Philion , Sanja Fidler NVIDIA, Vector Institute, University of Toronto ECCV 2020 --> --> The goal of perception for autonomous vehicles is to extract semantic representations from multiple sensors and fuse these representations into a single “bird’s-eye-view” coordinate frame for consumption by motion planning. We propose a new end-to-end architecture that directly extracts a bird’s-e

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