用内积变换高效编码点云，生成速度远超现有方法
Point Cloud Synthesis Using Inner Product Transforms
- 用内积变换捕捉点云几何拓扑特征
- 生成与重建任务表现优秀，推理速度提升数个数量级
- 适合需要高速点云生成的实时应用
点云合成，即从输入分布生成新点云，仍是一项挑战性任务，已有多种复杂机器学习模型被提出。本文提出一种新方法，通过内积编码点云的几何-拓扑特性，获得具有可证明表达能力的高效表示。该编码集成到深度学习模型中，在重建、生成和插值等典型任务中表现优异，推理时间比现有方法快数个数量级。
原文摘要 · Abstract (English)
Point cloud synthesis, i.e. the generation of novel point clouds from an input distribution, remains a challenging task, for which numerous complex machine learning models have been devised. We develop a novel method that encodes geometrical-topological characteristics of point clouds using inner products, leading to a highly-efficient point cloud representation with provable expressivity properties. Integrated into deep learning models, our encoding exhibits high quality in typical tasks like reconstruction, generation, and interpolation, with inference times orders of magnitude faster than existing methods.
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