3D Completion

3D completion aims to reconstruct missing parts of a 3D object or scene from incomplete data, a crucial task with applications in medical imaging and virtual/augmented reality. Recent research focuses on leveraging deep learning, particularly transformer-based models and generative adversarial networks (GANs), to achieve high-fidelity reconstructions, often incorporating advanced techniques like point cloud processing and the integration of textual descriptions for context-aware completion. These methods are improving the accuracy and efficiency of 3D reconstruction, enabling more realistic and detailed representations from limited input, with implications for various fields requiring 3D data analysis and manipulation. Furthermore, incorporating physics-based simulations, such as those mimicking ultrasound imaging, enhances the accuracy and realism of the completed 3D models.