Abstract
We present a latent diffusion model for fast feed-forward 3D scene generation. Given one or more images, our model Bolt3D directly samples a 3D scene representation in less than seven seconds on a single GPU. We achieve this by leveraging powerful and scalable existing 2D diffusion network architectures to produce consistent high-fidelity 3D scene representations. To train this model, we create a large-scale multiview-consistent dataset of 3D geometry and appearance by applying state-of-the-art dense 3D reconstruction techniques to existing multiview image datasets. Compared to prior multiview generative models that require per-scene optimization for 3D reconstruction, Bolt3D reduces the inference cost by a factor of up to 300 times.
Abstract (translated)
我们提出了一种用于快速前馈生成三维场景的潜在扩散模型。给定一个或多个图像，我们的模型Bolt3D可以直接在单个GPU上以不到七秒的时间采样出高质量的三维场景表示。通过利用强大的、可扩展的现有二维扩散网络架构，我们的模型能够生成一致且高保真的三维场景表示。为了训练这个模型，我们创建了一个大规模的多视角一致数据集，该数据集包含了3D几何和外观信息，这是通过对现有的多视图图像数据集应用最先进的密集三维重建技术得到的。与之前的需要针对每个场景进行三维重建优化的多视图生成模型相比，Bolt3D将推理成本降低了高达300倍。
URL
https://arxiv.org/abs/2503.14445
