{"ID":2863746,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.24986","arxiv_id":"2509.24986","title":"Light-SQ: Structure-aware Shape Abstraction with Superquadrics for Generated Meshes","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.","short_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,...","url_abs":"https://arxiv.org/abs/2509.24986","url_pdf":"https://arxiv.org/pdf/2509.24986v1","authors":"[\"Yuhan Wang\",\"Weikai Chen\",\"Zeyu Hu\",\"Runze Zhang\",\"Yingda Yin\",\"Ruoyu Wu\",\"Keyang Luo\",\"Shengju Qian\",\"Yiyan Ma\",\"Hongyi Li\",\"Yuan Gao\",\"Yuhuan Zhou\",\"Hao Luo\",\"Wan Wang\",\"Xiaobin Shen\",\"Zhaowei Li\",\"Kuixin Zhu\",\"Chuanlang Hong\",\"Yueyue Wang\",\"Lijie Feng\",\"Xin Wang\",\"Chen Change Loy\"]","published":"2025-09-29T16:18:32Z","proceeding":"cs.GR","tasks":"[\"cs.GR\",\"cs.AI\",\"cs.CV\"]","methods":"[]","has_code":false}