Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Нейронные поля излучения (NeRF)× | Сегментационная модель «Сегментируй всё»× | |
|---|---|---|
| Область | Глубокое обучение | Глубокое обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2020 | 2023 |
| Автор метода≠ | Ben Mildenhall | Alexander Kirillov |
| Тип | Neural network architecture | Neural network architecture |
| Основополагающий источник≠ | 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 ↗ | Kirillov, A., Mintun, E., Darrell, T., & Girshick, R. (2023). Segment Anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4015-4026). DOI ↗ |
| Другие названия | NeRF, Neural radiance field | SAM, Segment Anything |
| Связанные | 4 | 4 |
| Сводка≠ | 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. | Segment Anything Model (SAM) is a foundation model introduced by Kirillov et al. in 2023 that can segment any object in an image given various forms of prompts. SAM is trained on a massive dataset of diverse images and learns to segment objects based on minimal user input such as points, boxes, or text descriptions. |
| ScholarGateНабор данных ↗ |
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