Patch-based 3D Natural Scene Generation from a Single Example
April 25, 2023 Β· Entered Twilight Β· π Computer Vision and Pattern Recognition
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Repo contents: NN, README.md, colab_demo.ipynb, configs, docker, evaluation, generate.py, quick_inference_demo.py, utils
Authors
Weiyu Li, Xuelin Chen, Jue Wang, Baoquan Chen
arXiv ID
2304.12670
Category
cs.GR: Graphics
Cross-listed
cs.CV
Citations
17
Venue
Computer Vision and Pattern Recognition
Repository
https://github.com/wyysf-98/Sin3DGen
β 121
Last Checked
1 month ago
Abstract
We target a 3D generative model for general natural scenes that are typically unique and intricate. Lacking the necessary volumes of training data, along with the difficulties of having ad hoc designs in presence of varying scene characteristics, renders existing setups intractable. Inspired by classical patch-based image models, we advocate for synthesizing 3D scenes at the patch level, given a single example. At the core of this work lies important algorithmic designs w.r.t the scene representation and generative patch nearest-neighbor module, that address unique challenges arising from lifting classical 2D patch-based framework to 3D generation. These design choices, on a collective level, contribute to a robust, effective, and efficient model that can generate high-quality general natural scenes with both realistic geometric structure and visual appearance, in large quantities and varieties, as demonstrated upon a variety of exemplar scenes.
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