Evaluating the effect of image quantity on Gaussian Splatting: A statistical perspective

3D reconstruction is now a key capability in computer vision. With the advancements in NeRFs and Gaussian Splatting, there is an increasing need on properly capturing data to feed these algorithms and use them in real world scenarios. Most publicly available datasets that can be used for Gaussian Splatting are not suitable to do proper statistical analysis on reducing the number of cameras or the effect of uniformly placed cameras versus randomly placed cameras. The number of cameras in the scene significantly affects the accuracy and resolution of the final 3D reconstruction. Thus, designing a proper data capture system with a certain number of cameras is crucial for 3D reconstruction. In this paper UnrealGaussianStat dataset is introduced, and a statistical analysis is performed on decreasing viewpoints have on gaussian splatting. It is found that when the number of cameras is increased after 100 the train and test metrics saturates, and does not have significant impact on the reconstruction quality. • Developing a dataset suitable for studying the effect of reducing cameras have on 3D Gaussian Splatting. • Quantifying the effect of reducing the number of cameras have on the performance metrics for gaussian splatting by performing statistical analysis. • Finding a function that will fit the accuracy metrics when number of cameras are reduced.