Comparative Analysis of Deep Learning and Traditional Approaches for 3D Reconstruction

Advances in 3D reconstruction techniques have led to significant progress in various areas, including virtual reality, augmented reality, robotics, and more. However, their potential impact on medical imaging and diagnostic applications is also noteworthy. This article provides a detailed exploration of both traditional and modern methods in 3D reconstruction, aiming to explain their strengths, limitations, and potential impact on different fields. The article discusses the technical details of traditional SFM methods and modern NeRF-based approaches, comparing them to understand how they are evolving. By analyzing data from academic papers and tools like COLMAP and NerfStudio, the study offers insights into how effective and reliable these reconstruction techniques are. Experimental results show that NeRF-based methods, such as the Nerfacto model, perform better than traditional SFM and Multi-View Stereo (MVS) techniques in creating detailed point clouds. This superiority is particularly promising in industries like healthcare, where detailed 3D reconstructions are crucial for medical imaging and diagnostics. The research was conducted as part of the project APVV-21-0502: BrainWatch.