Enhancing Scene Realism through Neural Radiance Fields and Monocular Depth Estimation

This paper addresses the challenge of differentiable rendering, focusing on a novel implementation designed to integrate 3D objects seamlessly into reconstructed 3D environments, thereby creating entirely new perspectives of the scene. Our methodology leverages Neural Radiance Field (NeRF) models to reconstruct the 3D environments with high fidelity, alongside monocular depth estimation algorithms for deriving the 3D characteristics of objects from single images. The main goal of our approach lies in harmonizing the depth map output from the NeRF model with the depth data of the inserted object. This synergy enables the accurate and space-coherent placement of the object within the scene, ensuring a natural integration that enhances the overall realism of the virtual environment.