English

Bolt3D: Generating 3D Scenes in Seconds

Computer Vision and Pattern Recognition 2025-10-20 v2

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.

Keywords

Cite

@article{arxiv.2503.14445,
  title  = {Bolt3D: Generating 3D Scenes in Seconds},
  author = {Stanislaw Szymanowicz and Jason Y. Zhang and Pratul Srinivasan and Ruiqi Gao and Arthur Brussee and Aleksander Holynski and Ricardo Martin-Brualla and Jonathan T. Barron and Philipp Henzler},
  journal= {arXiv preprint arXiv:2503.14445},
  year   = {2025}
}

Comments

ICCV 2025. Project page: https://szymanowiczs.github.io/bolt3d

R2 v1 2026-06-28T22:25:34.663Z