用可变形超椭球提升点云分解精度与鲁棒性
SuperFlex: Deformable Superquadrics for Point Cloud Decomposition
- 引入新损失函数，显著提高重建精度
- 加入弯曲和锥形变形，更好表达曲面与不对称结构
- 在真实部分点云上表现稳定，适合工业应用
超椭球已证明能以紧凑且具几何意义的方式表示3D物体。然而，现有方法存在重建精度有限、仅支持刚性基元、对部分点云缺乏鲁棒性等问题。本文提出SuperFlex框架，增强超椭球分解的表达能力与适用性：首先设计新型损失函数，大幅提升重建精度；其次引入弯曲与锥形变形，实现对弯曲及非对称几何的高保真建模；最后利用高质量分解结果作为监督信号，训练出对真实世界部分点云具有鲁棒性的模型。实验表明，SuperFlex在重建精度上显著优于优化与学习基线方法，同时保持了极紧凑的基元表示。
原文摘要 · Abstract (English)
Superquadrics have proven to provide a compact, geometrically meaningful representation for 3D objects. However, existing methods suffer from limited reconstruction accuracy, are restricted to rigid primitives, and lack robustness to partial point clouds. In this work, we present SuperFlex, an enhanced framework that expands the expressive power and applicability of superquadric decompositions. First, we introduce a novel loss formulation which significantly improves reconstruction accuracy. Second, we include bending and tapering deformations, enabling high-fidelity representation of curved and asymmetric geometries. Finally, we leverage these high-quality decompositions as supervision to train a model that is robust to partial real-world point clouds. Experiments demonstrate substantial improvements in reconstruction accuracy over both optimization- and learning-based baselines while maintaining a highly compact primitive representation.
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