01541nam a22001577a 4500 629.8 Ullah Noor, Zabeeh 129051 ENHANCED NOVEL VIEW SYTHESIS VIA DEEP LEARNING-BASED 3D GAUSSIAN SPLATTING / Zabeeh Ullah Noor Islamabad: SMME- NUST. 2024. 83p. ; Soft Copy, 30cm. 3D Gaussian Splatting (3DGS) has emerged as a breakthrough in explicit radiance fields and computer graphics which has enabled precise scene representation, real time rendering, and efficient novel view synthesis. This paper explores the evolution of 3D rendering and recent advancements in 3DGS, with a particular focus on different techniques for synthesizing novel views with the incorporation of deep learning architectures especially transformers. To enhance scene quality, this research investigates the integration of monocular depth information during rendering and refines the loss function to improve reconstruction accuracy. By incorporating depth information our method enhances geometric details by capturing intricate details and reduction of artifacts. The findings contribute to the reconstruction of 3D scene with high fidelity, offering insights to optimize Gaussian Splatting technique for more efficient and realistic 3D rendering applications. MS Robotics and Intelligent Machine Engineering 119486 Supervisor : Dr Sara Baber 129046 http://10.250.8.41:8080/xmlui/handle/123456789/52946 ddc THE 613800 613800 0 0 0 0 SMME SMME EB 2025-05-22 0 629.8 SMME-TH-1132 2025-05-22 2025-05-22 THE