3D Generalization

3D generalization in deep learning focuses on enabling models trained on limited 3D data to accurately perform on unseen data or viewpoints. Current research emphasizes developing architectures and algorithms that leverage multi-view data, often employing contrastive learning or Gaussian distribution modeling to improve robustness and generalization across diverse datasets and object orientations. This work is crucial for advancing applications such as medical image analysis (e.g., prostate segmentation) and 3D object detection, where obtaining comprehensive annotated data is expensive and challenging, thereby improving the reliability and applicability of these technologies.