Object-Centric Domain Randomization for 3D Shape Reconstruction in the Wild
<|reference_start|>Object-Centric Domain Randomization for 3D Shape Reconstruction in the Wild: One of the biggest challenges in single-view 3D shape reconstruction in the wild is the scarcity of <3D shape, 2D image>-paired data from real-world environments. Inspired by remarkable achievements via domain randomization, we propose ObjectDR which synthesizes such paired data via a random simulation of visual variations in object appearances and backgrounds. Our data synthesis framework exploits a conditional generative model (e.g., ControlNet) to generate images conforming to spatial conditions such as 2.5D sketches, which are obtainable through a rendering process of 3D shapes from object collections (e.g., Objaverse-XL). To simulate diverse variations while preserving object silhouettes embedded in spatial conditions, we also introduce a disentangled framework which leverages an initial object guidance. After synthesizing a wide range of data, we pre-train a model on them so that it learns to capture a domain-invariant geometry prior which is consistent across various domains. We validate its effectiveness by substantially improving 3D shape reconstruction models on a real-world benchmark. In a scale-up evaluation, our pre-training achieves 23.6% superior results compared with the pre-training on high-quality computer graphics renderings.<|reference_end|>
@article{cho2024object-centric,
    title={Object-Centric Domain Randomization for 3D Shape Reconstruction in the
  Wild},
    author={Junhyeong Cho, Kim Youwang, Hunmin Yang, Tae-Hyun Oh},
    journal={arXiv preprint arXiv:2403.14539},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14539},
    primaryClass={cs.CV cs.AI cs.LG}
}