Evaluating Modern Approaches in 3D Scene Reconstruction: NeRF vs Gaussian-Based Methods

TL;DR

This paper compares Neural Radiance Fields (NeRF) and Gaussian-based methods for 3D scene reconstruction, highlighting their strengths and weaknesses in view synthesis, processing speed, and scene completeness, with implications for future research.

Contribution

It provides a comprehensive comparison of NeRF and Gaussian-based methods, including recent advancements like NICE-SLAM and SplaTAM, in terms of robustness and performance in complex environments.

Findings

NeRF excels in view synthesis but is slower.

Gaussian-based methods are faster but less complete.

Recent methods outperform traditional SLAM in robustness.

Abstract

Exploring the capabilities of Neural Radiance Fields (NeRF) and Gaussian-based methods in the context of 3D scene reconstruction, this study contrasts these modern approaches with traditional Simultaneous Localization and Mapping (SLAM) systems. Utilizing datasets such as Replica and ScanNet, we assess performance based on tracking accuracy, mapping fidelity, and view synthesis. Findings reveal that NeRF excels in view synthesis, offering unique capabilities in generating new perspectives from existing data, albeit at slower processing speeds. Conversely, Gaussian-based methods provide rapid processing and significant expressiveness but lack comprehensive scene completion. Enhanced by global optimization and loop closure techniques, newer methods like NICE-SLAM and SplaTAM not only surpass older frameworks such as ORB-SLAM2 in terms of robustness but also demonstrate superior performance in dynamic and complex environments. This comparative analysis bridges theoretical research with practical implications, shedding light on future developments in robust 3D scene reconstruction across various real-world applications.