3D Deep Learning

3D deep learning focuses on developing and applying deep learning models to analyze and interpret three-dimensional data, aiming to improve accuracy and efficiency in various applications. Current research emphasizes advancements in model architectures like convolutional neural networks (CNNs), transformers, and graph convolutional networks (GCNs), often incorporating techniques such as knowledge distillation and multi-dataset training to address challenges related to computational cost and data scarcity. This field is significantly impacting diverse domains, including medical imaging (e.g., Parkinson's disease diagnosis, cardiovascular analysis), robotics (e.g., spray painting, object manipulation), and remote sensing (e.g., forest inventory, object detection in point clouds), by enabling more accurate and automated analysis of complex 3D data.