ActiveNeuS: Active 3D Reconstruction using Neural Implicit Surface Uncertainty

Active learning in 3D scene reconstruction has been widely studied, as selecting informative training views is critical for the reconstruction. Recently, Neural Radiance Fields (NeRF) variants have shown performance increases in active 3D reconstruction using image rendering or geometric uncertainty. However, the simultaneous consideration of both uncertainties in selecting informative views remains unexplored, while utilizing different types of uncertainty can reduce the bias that arises in the early training stage with sparse inputs. In this paper, we propose ActiveNeuS, which evaluates candidate views considering both uncertainties. ActiveNeuS provides a way to accumulate image rendering uncertainty while avoiding the bias that the estimated densities can introduce. ActiveNeuS computes the neural implicit surface uncertainty, providing the color uncertainty along with the surface information. It efficiently handles the bias by using the surface information and a grid, enabling the fast selection of diverse viewpoints. Our method outperforms previous works on popular datasets, Blender and DTU, showing that the views selected by ActiveNeuS significantly improve performance.