A Critical Analysis of NeRF-Based 3D Reconstruction

This paper presents a critical analysis of image-based 3D reconstruction using neural radiance fields (NeRFs), with a focus on quantitative comparisons with respect to traditional photogrammetry. The aim is, therefore, to objectively evaluate the strengths and weaknesses of NeRFs and provide insights into their applicability to different real-life scenarios, from small objects to heritage and industrial scenes. After a comprehensive overview of photogrammetry and NeRF methods, highlighting their respective advantages and disadvantages, various NeRF methods are compared using diverse objects with varying sizes and surface characteristics, including texture-less, metallic, translucent, and transparent surfaces. We evaluated the quality of the resulting 3D reconstructions using multiple criteria, such as noise level, geometric accuracy, and the number of required images (i.e., image baselines). The results show that NeRFs exhibit superior performance over photogrammetry in terms of non-collaborative objects with texture-less, reflective, and refractive surfaces. Conversely, photogrammetry outperforms NeRFs in cases where the object's surface possesses cooperative texture. Such complementarity should be further exploited in future works.

Automated summary

This paper critically analyzes the use of neural radiance fields (NeRFs) for image-based 3D reconstruction and compares them quantitatively with traditional photogrammetry. The strengths and weaknesses of NeRFs are objectively evaluated, and their applicability to different real-life scenarios is discussed. The study compares various NeRF methods using objects with different sizes and surface characteristics, and evaluates the quality of the resulting 3D reconstructions based on multiple criteria. The results demonstrate the superior performance of NeRFs for non-collaborative objects with texture-less, reflective, and refractive surfaces, while photogrammetry outperforms NeRFs for objects with cooperative texture. The complementarity of these methods should be further explored in future research.