Neural Radiance Fields (NeRF) have shown impressive results in 3D reconstruction and generating novel views. A key challenge within NeRF is the editing of reconstructed scenes, such as object removal, which requires maintaining consistency across multiple views and ensuring high-quality synthesised perspectives. Previous studies have incorporated depth priors, typically from LiDAR or sparse depth measurements provided by COLMAP, to improve the performance of object removal in NeRF. However, these methods are either costly or time-consuming. In this paper, we propose a novel approach that integrates monocular depth estimates with NeRF-based object removal models to significantly reduce time consumption and enhance the robustness and quality of scene generation and object removal. We conducted a thorough evaluation of COLMAP's dense depth reconstruction on the KITTI dataset to verify its accuracy in depth map generation. Our findings suggest that COLMAP can serve as an effective alternative to a ground truth depth map where such information is missing or costly to obtain. Additionally, we integrated various monocular depth estimation methods into the removal NeRF model, i.e., SpinNeRF, to assess their capacity to improve object removal performance. Our experimental results highlight the potential of monocular depth estimation to substantially improve NeRF applications.

翻译：神经辐射场（NeRF）在三维重建和新视角生成领域展现了显著成效。其中，编辑重建场景（如物体移除）需要确保多视角一致性并生成高质量合成视角，成为NeRF面临的核心挑战。先前研究通过引入深度先验（通常来自LiDAR或COLMAP提供的稀疏深度测量）来提升NeRF中物体移除的性能，但这些方法要么成本高昂，要么耗时过长。本文提出一种创新方法，将单目深度估计与基于NeRF的物体移除模型相结合，显著降低时间消耗，同时增强场景生成和物体移除的鲁棒性与质量。我们在KITTI数据集上对COLMAP的稠密深度重建进行了系统评估，验证其在深度图生成中的准确性。研究结果表明，当真实深度信息缺失或获取成本过高时，COLMAP可成为有效替代方案。此外，我们将多种单目深度估计方法集成至移除NeRF模型SpinNeRF中，评估其提升物体移除性能的能力。实验成果凸显了单目深度估计在显著改善NeRF应用方面的潜力。