用超椭球体代替密集网格，实现快速高效3D生成
Rethinking 3D Shape Generation: Diffusion over Superquadrics
- 将扩散模型从密集几何空间转至紧凑的超椭球体参数空间
- 仅用7KB参数即可在0.6秒内生成高质量3D形状
- 支持无分辨率限制生成与部件级编辑，适合高可控场景
扩散模型虽推动了3D形状生成发展，但多数方法仍对高维表示（如体素/SDF网格、网格或点云）进行去噪，计算与内存开销大，难以提升分辨率或增强控制能力。本文重新思考扩散表示，提出将扩散过程从密集几何转向紧凑几何基元，将每个形状表示为一组超椭球体。相比数千至数百万个几何值的处理，仅需7KB的超椭球体参数（姿态、尺寸、形状），显著降低扩散状态维度及每步计算/内存开销。该方法在点云解码后，在标准基准上实现具有竞争力的表面保真度和分布性能，同时支持无分辨率限制的点云解码、部件级编辑与约束设计，绝大多数条件下生成耗时低于0.6秒。
原文摘要 · Abstract (English)
Diffusion models have advanced 3D shape generation, yet most methods still denoise in high-cardinality spaces (e.g., voxel/SDF grids, meshes, or point clouds), which is computationally and memory intensive and makes it difficult to scale in terms of both higher resolution and stronger controllability. We rethink the diffusion representation and propose to move diffusion from dense geometry to compact geometric primitives, representing each shape as a small set of superquadrics. Instead of operating on thousands to millions of geometric representation values, we leverage 7KB superquadric parameters (pose, size, and shape), drastically reducing diffusion-state dimensionality and per-step compute/memory. Our diffusion-over-superquadrics improves scalability by supporting broader capabilities (e.g., resolution-free point-cloud decoding, part-level editing, and constraint-based design) and achieving competitive surface-fidelity and distributional performance on standard benchmarks after point-cloud decoding, while enabling efficient generation within 0.6s per shape for most conditions.
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