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

• URL: http://arxiv.org/abs/2408.10789v1
• Date: Tue, 20 Aug 2024 12:30:37 GMT
• Title: Learning Part-aware 3D Representations by Fusing 2D Gaussians and Superquadrics
• Authors: Zhirui Gao, Renjiao Yi, Yuhang Huang, Wei Chen, Chenyang Zhu, Kai Xu,
• Abstract summary: Low-level 3D representations, such as point clouds, meshes, NeRFs, and 3D Gaussians, are commonly used to represent 3D objects or scenes. We aim to solve part-aware 3D reconstruction, which parses objects or scenes into semantic parts.
• Score: 16.446659867133977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-level 3D representations, such as point clouds, meshes, NeRFs, and 3D Gaussians, are commonly used to represent 3D objects or scenes. However, humans usually perceive 3D objects or scenes at a higher level as a composition of parts or structures rather than points or voxels. Representing 3D as semantic parts can benefit further understanding and applications. We aim to solve part-aware 3D reconstruction, which parses objects or scenes into semantic parts. In this paper, we introduce a hybrid representation of superquadrics and 2D Gaussians, trying to dig 3D structural clues from multi-view image inputs. Accurate structured geometry reconstruction and high-quality rendering are achieved at the same time. We incorporate parametric superquadrics in mesh forms into 2D Gaussians by attaching Gaussian centers to faces in meshes. During the training, superquadrics parameters are iteratively optimized, and Gaussians are deformed accordingly, resulting in an efficient hybrid representation. On the 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 are incorporated to model the complex texture and geometry details, ensuring high-quality rendering and geometry reconstruction. The reconstruction is fully unsupervised. We conduct extensive experiments on data from DTU and ShapeNet datasets, in which the method decomposes scenes into reasonable parts, outperforming existing state-of-the-art approaches.

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