CAD Model Retrieval

CAD model retrieval aims to efficiently locate relevant 3D CAD models from large databases, crucial for applications like design reuse and augmented reality. Current research focuses on developing robust and efficient retrieval methods using deep learning architectures, such as graph neural networks and convolutional neural networks, often incorporating contrastive learning or diffusion models for improved accuracy and speed. These advancements address challenges posed by the complexity of CAD data and the need for reduced reliance on manual labeling, ultimately improving the speed and accuracy of 3D model retrieval in various fields. The impact extends to streamlining industrial design processes, enhancing 3D scene understanding in robotics and AR/VR, and facilitating more effective case-based reasoning in engineering.