3D Transformation

3D transformation research focuses on accurately representing and manipulating three-dimensional objects and their relationships, often using 2D projections as input. Current efforts concentrate on developing robust algorithms for 3D object reconstruction and pose estimation from various data sources, including point clouds, images, and laser scans, employing techniques like neural implicit functions, geometric transformations (including rotations, translations, and scaling), and graph embeddings. These advancements are crucial for applications such as robotic manipulation, medical imaging (e.g., ultrasound), and knowledge graph representation, improving accuracy and robustness in these fields. The development of transformation-specific smoothing techniques also addresses the vulnerability of 3D models to adversarial attacks.