A Review of Neural Radiance Fields and 3D Gaussian Splatting for 3D Reconstruction

Abstract

Within the disciplines of computer vision and deep learning, the ability to construct 3D models from data has become a fundamental capability. A recent wave of progress has significantly advanced the two leading methods for scene representation:Neural Radiance Fields for implicit modeling and 3D Gaussian Splatting for explicit construction. This review aims to build a systematic cognitive framework of 3D reconstruction technology for readers by comparing the performance of these two technologies and their variants in static and dynamic scenes. This review selects representative evaluation parameters such as Peak Signal-to-Noise Ratio. By collecting experimental data from public datasets, it compares, organizes and summarizes the high-quality rendering ability of neural radiance field technology for geometric details and the high-speed real-time rendering ability of 3D Gaussian splatting. Looking ahead, this paper proposes key development directions such as the integration of expression paradigms and overcoming the slow speed of implicit expression, hoping to provide a structural knowledge framework and research inspiration for the professional field.