Merge branch 'master' of https://github.com/nv-tlabs/lift-splat-shoot

README.md

@@ -10,6 +10,8 @@ ECCV, 2020 (Poster)\
 **Abstract:**
 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 dense representations for motion planning, we show that the representations inferred by our model enable interpretable end-to-end motion planning by "shooting" template trajectories into a bird's-eye-view cost map output by our network. We benchmark our approach against models that use oracle depth from lidar. Project page: [https://nv-tlabs.github.io/lift-splat-shoot/](https://nv-tlabs.github.io/lift-splat-shoot/).
 
+**Questions/Requests:** Please file an [issue](https://github.com/nv-tlabs/lift-splat-shoot/issues) if you have any questions or requests about the code or the [paper](https://arxiv.org/abs/2008.05711). If you prefer your question to be private, you can alternatively email me at [email protected].
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 ### Citation
 If you found this codebase useful in your research, please consider citing
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