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| Neural Radiance Fields (NeRF)× | Vision Transformer× | |
|---|---|---|
| Tieteenala | Syväoppiminen | Syväoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2020 | 2021 |
| Kehittäjä≠ | Ben Mildenhall | Dosovitskiy, A. et al. |
| Tyyppi≠ | Neural network architecture | Transformer architecture for images (self-attention over patches) |
| Alkuperäislähde≠ | 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 ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Rinnakkaisnimet≠ | NeRF, Neural radiance field | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Liittyvät≠ | 4 | 5 |
| Tiivistelmä≠ | 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. | The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs). |
| ScholarGateAineisto ↗ |
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