Light-SQ: Structure-aware Shape Abstraction with Superquadrics for Generated Meshes
SIGGRAPH Asia 2025
- Yuhan Wang1
- Weikai Chen2,*
- Zeyu Hu2
- Runze Zhang2
- Yingda Yin2
- Ruoyu Wu2
- Keyang Luo2
- Shengju Qian2
- Yiyan Ma2
- Hongyi Li2
- Yuan Gao2
- Yuhuan Zhou2
- Hao Luo2
- Wan Wang2
- Xiaobin Shen2
- Zhaowei Li2
- Kuixin Zhu2
- Chuanlang Hong2
- Yueyue Wang2
- Lijie Feng2
- Xin Wang2,*
- Chen Change Loy1
1S-Lab, Nanyang Technological University 2 LIGHTSPEED
* Corresponding Authors
Abstract
In user-generated-content (UGC) applications, non-expert users often rely on image-to-3D generative models to create 3D assets. In this context, primitive-based shape abstraction offers a promising solution for UGC scenarios by compressing high-resolution meshes into compact, editable representations. Towards this end, effective shape abstraction must therefore be structure-aware, characterized by low overlap between primitives, part-aware alignment, and primitive compactness. We present Light-SQ, a novel superquadric-based optimization framework that explicitly emphasizes structure-awareness from three aspects. (a) We introduce SDF carving to iteratively udpate the target signed distance field, discouraging overlap between primitives. (b) We propose a block-regrow-fill strategy guided by structure-aware volumetric decomposition, enabling structural partitioning to drive primitive placement. (c) We implement adaptive residual pruning based on SDF update history to surpress over-segmentation and ensure compact results. In addition, Light-SQ supports multiscale fitting, enabling localized refinement to preserve fine geometric details. To evaluate our method, we introduce 3DGen-Prim, a benchmark extending 3DGen-Bench with new metrics for both reconstruction quality and primitive-level editability. Extensive experiments demonstrate that Light-SQ enables efficient, high-fidelity, and editable shape abstraction with superquadrics for complex generated geometry, advancing the feasibility of 3D UGC creation.