3D Reconstruction from JPG Images

Three-dimensional (3D) reconstruction from two-dimensional (2D) images is a fundamental challenge in computer vision and photogrammetry, with applications in medical imaging, robotics, and augmented reality. This research introduces an image-based modeling pipeline designed to overcome the inherent limitations of Joint Photographic Experts Group (JPEG) images, such as lossy compression and reduced structural fidelity. The proposed hybrid framework integrates photogrammetric methods specifically Structure-from-Motion (SFM) and Dense Stereo Matching with advanced point cloud generation and surface reconstruction techniques. Initially, Marching Cubes was utilized to generate dense point clouds from sequential JPEG slices, followed by Poisson Surface Reconstruction to produce watertight 3D models. Structural details are further enhanced using Structural Similarity index (SSIM) guided texture refinement. Evaluated on the Kaggle Chest CT Segmentation dataset, the method achieves an SSIM score of 0.725, outperforming the JPEG-based reconstruction baseline of 0.675 by 7.4%. In addition to improved accuracy, the study explores the balance between computational cost and reconstruction quality, offering insights relevant to real time and resource constrained applications. By bridging photogrammetry with computer vision, this work advances practical 3D reconstruction from compressed medical images, enabling efficient digitization in low-bandwidth environments.