Neural radiance field(NeRF)has become an important research area in computer graphics and computer vision in recent years.Due to its highly realistic visual synthesis effects,NeRF has been widely used in photorealistic rendering,virtual reality,human body modeling,urban mapping,and other domains.NeRF employs neural networks to learn implicit representations of 3D scenes from input image sets and to synthesize highly realistic novel view images.However,the training and inference speeds of the primitive NeRF model are very slow,posing challenges for real-condition deployment and application.To address the acceleration problem of NeRF,researchers have studied the acceleration of NeRF from the aspects of scene modeling methods and ray sampling strategies.Those works can be categorized into the following research directions:baking model,integrating models with discrete representation methods,enhancing sampling efficiency,using hash coding to reduce the complexity of MLP network,introducing scene generalization,and introducing deep supervision information and field decomposition methods.After introducing the background of the NeRF model,the advantages and characteristics of the representative methods of the above ideas were discussed and analyzed.Finally,the progress made in the acceleration of NeRF-related work and future prospects were summarized.
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