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Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction

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arxiv 2304.06714 v4 pith:QM5LM4EA submitted 2023-04-13 cs.CV

Hansheng Chen , Jiatao Gu , Anpei Chen , Wei Tian , Zhuowen Tu , Lingjie Liu , Hao Su This is my paper
classification cs.CV
keywords diffusiongenerationnerfreconstructionmodelpriorapproachimages
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3D-aware image synthesis encompasses a variety of tasks, such as scene generation and novel view synthesis from images. Despite numerous task-specific methods, developing a comprehensive model remains challenging. In this paper, we present SSDNeRF, a unified approach that employs an expressive diffusion model to learn a generalizable prior of neural radiance fields (NeRF) from multi-view images of diverse objects. Previous studies have used two-stage approaches that rely on pretrained NeRFs as real data to train diffusion models. In contrast, we propose a new single-stage training paradigm with an end-to-end objective that jointly optimizes a NeRF auto-decoder and a latent diffusion model, enabling simultaneous 3D reconstruction and prior learning, even from sparsely available views. At test time, we can directly sample the diffusion prior for unconditional generation, or combine it with arbitrary observations of unseen objects for NeRF reconstruction. SSDNeRF demonstrates robust results comparable to or better than leading task-specific methods in unconditional generation and single/sparse-view 3D reconstruction.

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Cited by 5 Pith papers

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  2. BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion

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    BoostDream refines coarse feed-forward text-to-3D assets via 3D distillation, multi-view SDS loss from a 2D diffusion model, and prompt-consistent normal maps to produce higher-quality results more efficiently than st...

  3. Predicting 3D structure by latent posterior sampling

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  4. Predicting 3D structure by latent posterior sampling

    cs.CV 2026-05 unverdicted novelty 5.0 of 10

    A latent-variable approach uses diffusion models on NeRF-encoded scene representations to perform posterior sampling for 3D reconstruction from single-view, multi-view, noisy, sparse-pixel, or sparse-depth inputs.

  5. Predicting 3D structure by latent posterior sampling

    cs.CV 2026-05 unverdicted novelty 5.0 of 10

    A two-stage method trains NeRF latents then a diffusion prior to sample posteriors for 3D reconstruction from varied observations including single-view, multi-view, noisy, sparse pixels, and sparse depth.

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