From 2D to 3D: Leveraging Sparse Inputs for High-Fidelity Model Generation with Neural Radiance Fields

Rendering 2D images into 3D models is a significant challenge in computer vision, with applications ranging from robotics to augmented reality. This paper presents a novel framework leveraging Neural Radiance Fields (NeRF) and its advancements to achieve efficient and high-fidelity 3D reconstruction. Our approach integrates feature extraction, ray sampling, and pose estimation using entropy-based optimization and attention-based aggregation, ensuring robust performance across diverse datasets. Key techniques include using PixelNeRF for few-shot rendering, iNeRF for pose refinement, and General Radiance Fields (GRF) for unseen geometries. Experiments demonstrate superior results in 3D representation accuracy, novel view synthesis, and generalization capabilities. This research highlights the potential of NeRF-based systems to revolutionize 3D modeling and content generation while addressing the limitations of traditional methods.