Predicting 3D structure by latent posterior sampling
The remarkable achievements of both generative models of 2D images and neural field representations for 3D scenes present a compelling opportunity to integrate the strengths of both approaches. In this work, we propose a methodology that combines a NeRF-based representation of 3D scenes with probabilistic modeling and reasoning using diffusion models. We view 3D reconstruction as a perception problem with inherent uncertainty that can thereby benefit from probabilistic inference methods. The core idea is to represent the 3D scene as a stochastic latent variable for which we can learn a prior and use it to perform posterior inference given a set of observations. We formulate posterior samp...
| Authors | Azmi Haider, Dan Rosenbaum |
|---|---|
| Primary category | cs.CV |
| Published | 11 May 2026 |
| Last updated at source | 20 May 2026 |
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