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DreamUDF: Generating Unsigned Distance Fields from A Single Image
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AI
DOI：10.1145/3687769.png)
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摘要
En 中文
Recent advances in diffusion models and neural implicit surfaces have shown promising progress in generating 3D models. However, existing generative frameworks are limited to closed surfaces, failing to cope with a wide range of commonly seen shapes that have open boundaries. In this work, we present DreamUDF, a novel framework for generating high-quality 3D objects with arbitrary topologies from a single image. To address the challenge of generating proper topology given sparse and ambiguous observations, we propose to incorporate both the data priors from a multi-view diffusion model and the geometry priors brought by an unsigned distance field (UDF) reconstructor. In particular, we leverage a joint framework that consists of 1) a generation module that produces a neural radiance field for photo realistic renderings from arbitrary views; and 2) a reconstruction module that distills the learnable radiance field into surfaces with arbitrary topologies. We further introduce a field coupler that bridges the radiance field and UDF under a novel optimization scheme. This allows the two modules to mutually boost each other during training. Extensive experiments and evaluations demonstrate that DreamUDF achieves high-quality reconstruction and robust 3D generation on both closed and open surfaces with arbitrary topologies, compared to the previous works.
Keyword:
Unsigned distance field
single-view 3D generation
diffusion model
neural radiance field
期刊
IF:
9.5
论文数:
4.7K
被引数:
3.6W
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