Review of 3D Scene Reconstruction: From Traditional Methods to Advanced Deep Learning Models

Abstract

3D scene reconstruction represents a pivotal domain within computer vision, involving a diverse array of techniques ranging from classical geometry-driven approaches to modern deep learning-based models. This review article offers an extensive summary of cutting-edge methods for 3D reconstruction, including techniques such as Structure-from-Motion (SfM) and Multi-View Stereo (MVS), as well as cutting-edge deep learning techniques including CNNs, GANs,Variational Autoencoders (VAEs) and Neural Radiance Fields (NeRF). We analyze the strengths and limitations of each approach, particularly in terms of accuracy, efficiency, generalization, and their ability to handle complex scenes. Furthermore, we delve into the key challenges faced in 3D scene reconstruction, including the trade-offs between computational efficiency and model accuracy, the generalization to diverse environments, and the integration of multi-modal data sources. Special attention is given to NeRF, a breakthrough in the field, discussing its current capabilities and potential areas for improvement in future research. This review aims to serve as a resource for researchers and practitioners by summarizing the current landscape of 3D reconstruction technologies and identifying promising directions for future exploration.