PartGS:Learning Part-aware 3D Representations by Fusing 2D Gaussians and Superquadrics

원문 초록

Low-level 3D representations, such as point clouds, meshes, NeRFs, and 3D Gaussians, are commonly used to represent 3D objects or scenes. However, human perception typically understands 3D objects at a higher level as a composition of parts or structures rather than points or voxels. Representing 3D objects or scenes as semantic parts can benefit further understanding and applications. In this paper, we introduce $\textbf{PartGS}$, $\textbf{part}$-aware 3D reconstruction by a hybrid representation of 2D $\textbf{G}$aussians and $\textbf{S}$uperquadrics, which parses objects or scenes into semantic parts, digging 3D structural clues from multi-view image inputs. Accurate structured geometry reconstruction and high-quality rendering are achieved at the same time. Our method simultaneously optimizes superquadric meshes and Gaussians by coupling their parameters within our hybrid representation. On one hand, this hybrid representation inherits the advantage of superquadrics to represent different shape primitives, supporting flexible part decomposition of scenes. On the other hand, 2D Gaussians capture complex texture and geometry details, ensuring high-quality appearance and geometry reconstruction. Our method is fully unsupervised and outperforms existing state-of-the-art approaches in extensive experiments on DTU, ShapeNet, and real-life datasets.