Neural Radiance Field-Based Visual Rendering: A Comprehensive Review.

Area of Science:

• Computer Vision
• Computer Graphics
• Artificial Intelligence

Background:

• Neural Radiance Fields (NeRF) represent a significant advancement in neural implicit representations.
• NeRF technology has revolutionized 3D reconstruction, rendering, and dynamic scene modeling.
• Existing research faces fragmentation and unclear technical pathways, hindering widespread adoption.

Purpose of the Study:

• To provide a systematic framework for understanding Neural Radiance Fields (NeRF).
• To survey the theoretical foundations, benchmark datasets, methodological advances, and application scenarios of NeRF.
• To clarify technical pathways and address cross-domain fragmentation in NeRF research.

Main Methods:

• Analysis of NeRF's core mechanisms, including radiance field modeling and differentiable volume rendering.
• Definition and standardization of evaluation benchmarks for NeRF models.
• Charting evolutionary pathways in model optimization, input adaptation, and dynamic scene modeling.
• Analysis of interconnections between key NeRF methodologies.

Main Results:

• A comprehensive survey of NeRF's theoretical underpinnings and core components.
• Identification of standardized evaluation benchmarks for consistent NeRF performance assessment.
• Mapping of methodological advancements and their relationships across different NeRF techniques.
• Insights into task-specific applications, migration bottlenecks, and potential solutions.

Conclusions:

• The presented framework systematically organizes NeRF knowledge, addressing fragmentation.
• NeRF technology offers significant potential across digital content creation and embodied perception.
• Future research should focus on theoretical refinements and cross-disciplinary deployment of NeRF-based technologies.