DMV3D：使用3D大型重建模型进行去噪的多视角扩散

DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model

November 15, 2023

作者: Yinghao Xu, Hao Tan, Fujun Luan, Sai Bi, Peng Wang, Jiahao Li, Zifan Shi, Kalyan Sunkavalli, Gordon Wetzstein, Zexiang Xu, Kai Zhang

cs.AI

摘要

我们提出了DMV3D，一种新颖的3D生成方法，它使用基于Transformer的3D大型重建模型来去噪多视角扩散。我们的重建模型融合了三面NeRF表示，并可以通过NeRF重建和渲染去噪多视角图像，实现在单个A100 GPU上的sim30s内的单阶段3D生成。我们在大规模多视角图像数据集上训练DMV3D，这些数据集包含高度多样化的对象，仅使用图像重建损失，而无需访问3D资产。我们展示了针对需要对未见对象部分进行概率建模以生成具有清晰纹理的多样化重建的单图像重建问题的最新结果。我们还展示了高质量的文本到3D生成结果，优于先前的3D扩散模型。我们的项目网站位于：https://justimyhxu.github.io/projects/dmv3d/。

English

We propose DMV3D, a novel 3D generation approach that uses a transformer-based 3D large reconstruction model to denoise multi-view diffusion. Our reconstruction model incorporates a triplane NeRF representation and can denoise noisy multi-view images via NeRF reconstruction and rendering, achieving single-stage 3D generation in sim30s on single A100 GPU. We train DMV3D on large-scale multi-view image datasets of highly diverse objects using only image reconstruction losses, without accessing 3D assets. We demonstrate state-of-the-art results for the single-image reconstruction problem where probabilistic modeling of unseen object parts is required for generating diverse reconstructions with sharp textures. We also show high-quality text-to-3D generation results outperforming previous 3D diffusion models. Our project website is at: https://justimyhxu.github.io/projects/dmv3d/ .