000			01541nam a22001577a 4500
082			_a629.8
100			_aUllah Noor, Zabeeh _9129051
245			_aENHANCED NOVEL VIEW SYTHESIS VIA DEEP LEARNING-BASED 3D GAUSSIAN SPLATTING / _c Zabeeh Ullah Noor
264			_aIslamabad: _bSMME- NUST. _c2024.
300			_a83p. ; _bSoft Copy, _c30cm.
500			_a3D 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.
650			_aMS Robotics and Intelligent Machine Engineering _9119486
700			_aSupervisor : Dr Sara Baber _9129046
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/52946
942			_2ddc _cTHE
999			_c613800 _d613800