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| Νευρωνικά Πεδία Ακτινοβολίας (NeRF)× | Μοντέλα Διάχυσης σε Λανθάνοντα Χώρο× | |
|---|---|---|
| Πεδίο | Βαθιά Μάθηση | Βαθιά Μάθηση |
| Οικογένεια | Machine learning | Machine learning |
| Έτος προέλευσης≠ | 2020 | 2022 |
| Δημιουργός≠ | Ben Mildenhall | Robin Rombach |
| Τύπος | Neural network architecture | Neural network architecture |
| Θεμελιώδης πηγή≠ | Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing scenes as neural radiance fields for view synthesis. In Computer Vision-ECCV 2020: 16th European Conference (pp. 405-421). Springer International Publishing. DOI ↗ | Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684-10695). DOI ↗ |
| Εναλλακτικές ονομασίες≠ | NeRF, Neural radiance field | LDM, Stable Diffusion, Latent Diffusion |
| Συναφείς | 4 | 4 |
| Σύνοψη≠ | Neural Radiance Fields (NeRF) is a method introduced by Mildenhall et al. in 2020 that represents a 3D scene as a continuous function parameterized by a neural network. Given multi-view images of a scene, NeRF learns to predict the color and density of light rays at any spatial location and viewing angle, enabling novel view synthesis with photorealistic quality. | Latent Diffusion Models (LDMs) are a generative approach introduced by Rombach et al. in 2022 that performs the diffusion process in a compressed latent space rather than pixel space, enabling efficient high-resolution image synthesis. By compressing images into a low-dimensional latent representation using a variational autoencoder, diffusion becomes computationally tractable while maintaining visual quality. |
| ScholarGateΣύνολο δεδομένων ↗ |
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