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Recently, the publication of the Neural Radiance Field (NeRF) has sparked a surge in further research, unveiling a wealth of innovative solutions in related domains. With its potent predictive capabilities and operational efficiency, neural networks have addressed the traditional trade-off dilemma, enabling real-time rendering at state-of-the-art (SOTA) quality. This breakthrough has introduced a novel rendering approach in computer graphics (CG) and unlocked fresh opportunities for real-time applications such as video games, augmented reality (AR), and virtual reality (VR). This paper aims to offer a comprehensive overview of recent NeRF-like methodologies and explore potential pathways for enhancing NeRF to achieve both real-time performance and photorealistic standards. Our study includes a comparative analysis of these methodologies in terms of their efficacy and hardware requirements. Ultimately, this paper outlines potential future advancements in this field. The objective of this paper is to familiarize both newcomers and researchers with NeRF, catalyze in-depth investigations, propose enhanced methodologies.
Shunyu Yang (2024) studied this question.