Part Level Reconstruction

Part-level reconstruction focuses on representing 3D objects not as monolithic entities, but as assemblies of individual parts, enabling more nuanced understanding and manipulation. Current research employs neural implicit representations, often combined with techniques like superquadrics and Gaussian distributions, to model both the shape and appearance of these parts, leveraging multi-view images or even single-view inputs and diffusion models. This approach improves 3D reconstruction accuracy, facilitates tasks like object articulation and motion analysis, and has significant implications for robotics, computer graphics, and other fields requiring detailed 3D scene understanding. The ability to decompose objects into meaningful parts enhances applications such as 3D shape editing, feature-preserving reconstruction, and robot interaction planning.