The generation of high-quality 3D models from single 2D images remains challenging in terms of accuracy and completeness. Deep learning has emerged as a promising solution, offering new avenues for improvements. However, building models from scratch is computationally expensive and requires large datasets. This paper presents a transfer-learning-based approach for category-specific 3D reconstruction from a single 2D image. The core idea is to fine-tune a pre-trained model on specific object categories using new, unseen data, resulting in specialized versions of the model that are better adapted to reconstruct particular objects. The proposed approach utilizes a three-phase pipeline comprising image acquisition, 3D reconstruction, and refinement. After ensuring the quality of the input image, a ResNet50 model is used for object recognition, directing the image to the corresponding category-specific model to generate a voxel-based representation. The voxel-based 3D model is then refined by transforming it into a detailed triangular mesh representation using the Marching Cubes algorithm and Laplacian smoothing. An experimental study, using the Pix2Vox model and the Pascal3D dataset, has been conducted to evaluate and validate the effectiveness of the proposed approach. Results demonstrate that category-specific fine-tuning of Pix2Vox significantly outperforms both the original model and the general model fine-tuned for all object categories, with substantial gains in Intersection over Union (IoU) scores. Visual assessments confirm improvements in geometric detail and surface realism. These findings indicate that combining transfer learning with category-specific fine tuning and refinement strategy of our approach leads to better-quality 3D model generation.

This study presents an advanced method for post-mortem person identification using the segmentation of skeletal structures from chest X-ray images. The proposed approach employs the Attention U-Net architecture, enhanced with gated attention mechanisms, to refine segmentation by emphasizing spatially relevant anatomical features while suppressing irrelevant details. By isolating skeletal structures which remain stable over time compared to soft tissues, this method leverages bones as reliable biometric markers for identity verification. The model integrates custom-designed encoder and decoder blocks with attention gates, achieving high segmentation precision. To evaluate the impact of architectural choices, we conducted an ablation study comparing Attention U-Net with and without attention mechanisms, alongside an analysis of data augmentation effects. Training and evaluation were performed on a curated chest X-ray dataset, with segmentation performance measured using Dice score, precision, and loss functions, achieving over 98% precision and 94% Dice score. The extracted bone structures were further processed to derive unique biometric patterns, enabling robust and privacy-preserving person identification. Our findings highlight the effectiveness of attention mechanisms in improving segmentation accuracy and underscore the potential of chest bone-based biometrics in forensic and medical imaging. This work paves the way for integrating artificial intelligence into real-world forensic workflows, offering a non-invasive and reliable solution for post-mortem identification.