Transfer Learning-based Approach for 3D Reconstruction from a Single 2D Image

The field of 3D reconstruction faces persistent challenges in achieving high-quality models, often hindered by issues related to accuracy, completeness, and real-time generation. To address these issues, deep learning has emerged as a promising solution. However, building and training deep learning models from scratch requires substantial computational costs and large datasets. In this context, transfer learning proves advantageous in mitigating these challenges by leveraging pre-trained models on related domains. In this paper, we present a transfer learning-based approach for 3D reconstruction from a single 2D image. Our approach involves fine-tuning pre-trained models using unseen, high-quality datasets to enhance the accuracy, completeness, and robustness of real-world object reconstructions. In order to demonstrate the effectiveness of our approach, we conducted an experimental study that involved retraining the Pix2Vox model using the pascal3D dataset. Through various metrics and a visual comparison, we demonstrate the advancements achieved by our retrained model over the initial pre-trained one. The obtained results demonstrate improvements in accuracy and completeness.