3D Part Assembly

3D part assembly research focuses on automatically assembling multiple components into a complete structure, addressing the computational complexity of finding optimal configurations. Current approaches leverage deep learning models, particularly transformers and score-based generative methods, to predict part poses and sequences, often incorporating geometric reasoning and instance encoding to handle similar parts. These advancements aim to improve robotic assembly automation across various applications, from manufacturing to furniture assembly, by enhancing efficiency and reducing reliance on pre-programmed instructions. The development of more robust and efficient algorithms is crucial for broader adoption in real-world scenarios.