NERF FOR 3D RECONSTRUCTION USING DEEP LEARNING TECHNIQUES

Abstract

3D scene reconstruction has become an essential component of applications in virtual reality, augmented reality, gaming, and digital content creation. This project presents a novel approach to 3D scene reconstruction leveraging Neural Radiance Fields (NeRF), a state-of-the-art deep learning framework capable of synthesizing photorealistic views from multiple 2D images. The proposed system takes a set of scene images along with their camera poses and trains a neural network to represent the scene as a continuous volumetric function. To enhance reconstruction quality, the approach integrates advanced image preprocessing, optimized ray sampling, and depth aware volume rendering, leading to improved spatial detail preservation and more accurate depth perception. Unlike conventional NeRF based methods that suffer from inconsistent details and reliance on synthetic data, this work focuses on real-scene reconstruction with improved fidelity and reduced artifacts. Implemented using TensorFlow and evaluated on synthetic LEGO scene data, the system effectively produces high-quality 3D reconstructions and generates novel viewpoints with realistic visual consistency. The final output offers interactive visualization and rendered video, demonstrating its potential for immersive digital content applications.