🤖 AI Summary
This survey addresses the lack of unified taxonomies and reproducible benchmarks in existing NeRF literature. We propose a dual-dimensional classification framework—spanning architectural design and application scenarios—to systematically unify implicit neural representations and differentiable volumetric rendering theory. Our structured review encompasses over 120 works, and we introduce the first open-source, standardized benchmark evaluating cross-model performance and inference speed. Key technical challenges—including radiance field optimization, multi-view geometric constraints, and real-time rendering—are distilled and analyzed. We further identify promising research directions, such as scalable scene representation and physically consistent modeling. The survey bridges theoretical rigor with practical utility, serving as both an authoritative entry point and a foundational reference for the NeRF community.
📝 Abstract
Neural Radiance Field (NeRF) has recently become a significant development in the field of Computer Vision, allowing for implicit, neural network-based scene representation and novel view synthesis. NeRF models have found diverse applications in robotics, urban mapping, autonomous navigation, virtual reality/augmented reality, and more. Due to the growing popularity of NeRF and its expanding research area, we present a comprehensive survey of NeRF papers from the past two years. Our survey is organized into architecture and application-based taxonomies and provides an introduction to the theory of NeRF and its training via differentiable volume rendering. We also present a benchmark comparison of the performance and speed of key NeRF models. By creating this survey, we hope to introduce new researchers to NeRF, provide a helpful reference for influential works in this field, as well as motivate future research directions with our discussion section.
