Outdoor Scene Extrapolation with Hierarchical Generative Cellular Automata
Dongsu Zhang, Francis Williams, Zan Gojcic, Karsten Kreis, Sanja Fidler, Young Min Kim, Amlan Kar
Abstract
We aim to generate fine-grained 3D geometry from large-scale sparse LiDAR scans abundantly captured by autonomous vehicles (AV). Contrary to prior work on AV scene completion we aim to extrapolate fine geometry from unlabeled and beyond spatial limits of LiDAR scans taking a step towards generating realistic high-resolution simulation-ready 3D street environments. We propose hierarchical Generative Cellular Automata (hGCA) a spatially scalable conditional 3D generative model which grows geometry recursively with local kernels following GCAs in a coarse-to-fine manner equipped with a light-weight planner to induce global consistency. Experiments on synthetic scenes show that hGCA generates plausible scene geometry with higher fidelity and completeness compared to state-of-the-art baselines. Our model generalizes strongly from sim-to-real qualitatively outperforming baselines on the Waymo-open dataset. We also show anecdotal evidence of the ability to create novel objects from real-world geometric cues even when trained on limited synthetic content.
BibTeX
@inproceedings{cvpr2024_outdoorsceneextr,
title = {Outdoor Scene Extrapolation with Hierarchical Generative Cellular Automata},
author = {Dongsu Zhang and Francis Williams and Zan Gojcic and Karsten Kreis and Sanja Fidler and Young Min Kim and Amlan Kar},
booktitle = {CVPR 2024},
year = {2024}
}