  Neural Radiance Fields (NeRF) have achieved impressive results in 3D
reconstruction and novel view generation. A significant challenge within NeRF
involves editing reconstructed 3D scenes, such as object removal, which demands
consistency across multiple views and the synthesis of high-quality
perspectives. Previous studies have integrated depth priors, typically sourced
from LiDAR or sparse depth estimates from COLMAP, to enhance NeRF's performance
in object removal. However, these methods are either expensive or
time-consuming. This paper proposes a new pipeline that leverages SpinNeRF and
monocular depth estimation models like ZoeDepth to enhance NeRF's performance
in complex object removal with improved efficiency. A thorough evaluation of
COLMAP's dense depth reconstruction on the KITTI dataset is conducted to
demonstrate that COLMAP can be viewed as a cost-effective and scalable
alternative for acquiring depth ground truth compared to traditional methods
like LiDAR. This serves as the basis for evaluating the performance of
monocular depth estimation models to determine the best one for generating
depth priors for SpinNeRF. The new pipeline is tested in various scenarios
involving 3D reconstruction and object removal, and the results indicate that
our pipeline significantly reduces the time required for the acquisition of
depth priors for object removal and enhances the fidelity of the synthesized
views, suggesting substantial potential for building high-fidelity digital twin
systems with increased efficiency in the future.
