000			02144nam a22001577a 4500
082			_a629.8
100			_aIqra Asghar _9129047
245			_a3D Reconstruction using Machine Learning / _c Asghar, Iqra
264			_aIslamabad : _bSMME- NUST; _c2025
300			_a84p. _bSoft Copy _c30cm
500			_aThe field of 3D reconstruction has undergone significant advancements with the integration of machine learning techniques, enabling more efficient and accurate modeling of complex environments. This research focuses on leveraging cutting-edge methodologies, such as 3D Gaussian Splatting, to address challenges in real-time rendering and dynamic scene reconstruction. Traditional methods for dynamic scene rendering often fall short in maintaining high-quality and real-time performance, especially for complex, moving environments. In contrast, Gaussian Splatting employs probabilistic primitives to represent 3D point clouds, offering a balance between computational efficiency and visual quality. This study extends Progressive Gaussian Splatting to dynamic environments by introducing a framework that ensures temporal coherence and real-time performance. The proposed methodology employs a hybrid geometric representation, progressive propagation for Gaussian refinement, and deformation fields encoded via multi-resolution voxel grids to capture motion. Evaluations on synthetic and real-world datasets demonstrate significant improvements in rendering quality and temporal coherence, achieving state-of-the-art results in metrics such as PSNR (41.99), SSIM (0.995), and LPIPS (0.011) for synthetic dataset and increased PSNR by 1.79 and SSIM by 0.046 for real world’s hypernerf dataset. The research provides new insights into the practical application of Gaussian Splatting in dynamic environments, opening avenues for enhanced virtual reality (VR), augmented reality (AR), and robotics applications.
650			_aMS Robotics and Intelligent Machine Engineering _9119486
700			_aSupervisor : Dr. Sara Baber _9129046
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/52411
942			_2ddc _cTHE
999			_c613797 _d613797