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Surv."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>View synthesis is a fundamental task in computer vision, known for its significantly higher complexity compared to conventional vision problems. The introduction of Neural Radiance Fields (NeRF) marked a major breakthrough in this field, substantially improving previous methods and pushing view synthesis to unprecedented levels. This survey aims at systematically reviewing the progress of NeRF-based models in computer vision. We begin by explaining the core principles underlying the success of NeRF. Then, we delve into and analyze seven representative NeRF-based representation forms, including Implicit Representation, Neural Point Cloud, and others. Next, we provide a comprehensive comparison and analysis of 14 major research directions that enhance NeRF, such as Modeling Different Practical Capturing Scenarios, Generalization in Modeling, and Modeling Dynamic Scenes. 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