Comparative Insights into NeRF 3D Scalability

Abstract

3-D reconstruction for large scale indoor and outdoor environments constituted a challenging problem in inverse graphics and computer vision research community. The scalability issue that concerns only the geographic dimensions of the reconstructed scene in terms of scale is a inevitable bottleneck. This could be confronted by state-of-the-art methods which a comparative study should be reveal new insights of the digital environment representation. In this paper, a comparative study is facilitated including few literature methods with accordance by common evaluation metrics and computational complexity. A novel loss function referring to the scene details is also discussed by accessing the density variable of the NeRF multilayer perceptron output as alternative research direction. To this end, it presents efficiency advantange in terms of robustness to noise, scale and sentivity to distortions combined with sufficiency on accessed GP-GPUs memory and footprint in several datasets while it was validated and compared with state-of-the-art Inf-NeRF, Grid-NeRF and Nerfacto techniques.