Synthetic 3D

Synthetic 3D data generation is rapidly advancing, aiming to overcome limitations of real-world data acquisition for various computer vision tasks. Current research focuses on creating realistic synthetic datasets using physically-based simulations, generative adversarial networks (GANs), and procedural generation techniques, often coupled with domain adaptation methods to bridge the gap between synthetic and real data. These efforts are significantly impacting fields like robotics, medical imaging, and autonomous driving by providing large, labeled datasets for training deep learning models, thereby improving accuracy and reducing the need for expensive and time-consuming manual annotation. The resulting models demonstrate improved performance in tasks such as 3D object detection, reconstruction, and segmentation.