This paper introduces a novel approach for jointly performing image denoising and 3D point cloud reconstruction from single noisy medical images. By leveraging a joint learning framework, our model improves the accuracy and robustness of 3D reconstructions, particularly under noisy conditions. The model employs a two-branch architecture: a denoising branch utilizing convolutional layers with dropout regularization and a reconstruction branch that predicts 3D point clouds. Quantitative experiments on the LIDC-IDRI dataset demonstrate the model's superior performance compared to sequential baselines, achieving a 15% improvement in PSNR and a 20% reduction in Chamfer Distance. These results highlight the potential clinical applications of our framework in surgical planning and anatomical modeling.

Situation Awareness (SA) is critical for effective decision-making in complex, dynamic environments. This paper introduces a novel hybrid generative framework combining theoretical advancements in Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) with domainspecific preprocessing techniques for synthetic data generation in SA training. We evaluate the framework's performance using metrics such as Frechet Inception Distance (FID) and a proposed Situational Fidelity Score (SFS). Applications in autonomous driving and cybersecurity demonstrate improved system performance, including enhanced anomaly detection and decision-making accuracy. This research advances generative AI methodologies while addressing the data scarcity challenges in SA systems, enabling robust and adaptable training environments. Impact Statement-This research specifically advances the state of the art in SA training by introducing a novel framework that integrates advanced generative models, such as GANs and VAEs, tailored for the unique requirements of SA systems. Unlike traditional data augmentation methods, this approach systematically addresses the scarcity and sensitivity of training data by producing synthetic datasets that closely replicate realworld complexities. The proposed framework also emphasizes the generation of rare and anomalous events, which are critical for improving the robustness and adaptability of SA algorithms. Furthermore, it outlines rigorous validation techniques and domainspecific adaptations, ensuring the fidelity and applicability of synthetic data in operational environments. By bridging gaps in data availability and diversity, this work not only enhances training efficiency but also fosters innovation in the development of collaborative multi-agent systems and autonomous operations for complex, dynamic scenarios.