Papers › Pyramid Diffusion for Fine 3D Large Scene Generation

Pyramid Diffusion for Fine 3D Large Scene Generation

20 Nov 2023arXiv:2311.12085archive 2025-07-28

Yuheng Liu, Xinke Li, Xueting Li, Lu Qi, Chongshou Li, Ming-Hsuan Yang

Diffusion models have shown remarkable results in generating 2D images and small-scale 3D objects. However, their application to the synthesis of large-scale 3D scenes has been rarely explored. This is mainly due to the inherent complexity and bulky size of 3D scenery data, particularly outdoor scenes, and the limited availability of comprehensive real-world datasets, which makes training a stable scene diffusion model challenging. In this work, we explore how to effectively generate large-scale 3D scenes using the coarse-to-fine paradigm. We introduce a framework, the Pyramid Discrete Diffusion model (PDD), which employs scale-varied diffusion models to progressively generate high-quality outdoor scenes. Experimental results of PDD demonstrate our successful exploration in generating 3D scenes both unconditionally and conditionally. We further showcase the data compatibility of the PDD model, due to its multi-scale architecture: a PDD model trained on one dataset can be easily fine-tuned with another dataset. Code is available at https://github.com/yuhengliu02/pyramid-discrete-diffusion.

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conv1x1x3 yuhengliu02/pyramid-discrete-diffusion/models/latent_diffusion/stage1/model.py official repository ran · our draft was wrong MIT (permissive) · aa5dea4c4ceff835 · report
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log_1_min_a Yuheng-SWJTU/pyramid-discrete-diffusion/models/conditional_diffusion/con_diffusion.py official repository ran · honoured contract fingerprinted MIT (permissive) · 479771b269fc5883 · report
log_add_exp Yuheng-SWJTU/pyramid-discrete-diffusion/models/conditional_diffusion/con_diffusion.py official repository ran · honoured contract fingerprinted MIT (permissive) · 032c0dece6594020 · report
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sum_except_batch Yuheng-SWJTU/pyramid-discrete-diffusion/models/conditional_diffusion/con_diffusion.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 5eb945b1f970f6af · report
dice_coef_multilabel Yuheng-SWJTU/pyramid-discrete-diffusion/utils/loss.py official repository unverified MIT (permissive) · 3430f8725f3e6bd1 · report

Tasks

Scene Generation

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Methods

Diffusion

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