विधियों की तुलना करें

	न्यूरल रेडियंस फील्ड्स (NeRF)	विजन ट्रांसफार्मर
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2020	2021
प्रवर्तक	Ben Mildenhall	Dosovitskiy, A. et al.
प्रकार	Neural network architecture	Transformer architecture for images (self-attention over patches)
मौलिक स्रोत	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.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
उपनाम	NeRF, Neural radiance field	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
संबंधित	4	5
सारांश	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).