Neural Radiance Field Reconstruction in Complex Scenes Without Camera Parameter Dependency
Year: 2025
Type: conference-paper
Abstract: Neural Radiance Field (NeRF), which has been introduced since 2020, enables realistic 3D reconstruction from calibrated images. NeRF inspired global research into Neural Volumetric Rendering (NVR). In this work, we will first explore NVR's foundations, its ties to image-based rendering and inverse graphics, and then evaluate three methods: NeRF, 3D Gaussian Splatting and Gaussian Shader. As a result, 3D Gaussian Splatting excels in rendering speed, while NeRF offers accessible 3D training. In addition, Gaussian Shader excels in handling reflective surfaces but struggles with complex scenes, where the other methods prove more robust.
Authors: Vuong-Minh Nguyen Hoang, Hung Viet Nguyen, Kien Trung Luong
Institution: FPT University
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414651286