论文标题
Advectivionet：用于点云处理的Eulerian-Lagrangian流体水库
AdvectiveNet: An Eulerian-Lagrangian Fluidic reservoir for Point Cloud Processing
论文作者
论文摘要
本文提出了一种新型的物理启发的深度学习方法，用于由流体力学中自然流动现象动机的点云处理。我们的学习结构使用静态背景网格和Lagrangian材料空间共同定义了欧拉世界空间中的数据，并使用移动粒子定义了数据。通过引入这种Eulerian-Lagrangian表示，我们能够使用从广义高维力场产生的流速度自然发展和积累粒子特征。我们通过解决各种点云分类和细分问题来证明该系统的功效。整个几何储层和数据流都模仿了建模自然流动过程中经典图片/翻转方案的管道，弥合了几何机器学习和物理模拟的学科。
This paper presents a novel physics-inspired deep learning approach for point cloud processing motivated by the natural flow phenomena in fluid mechanics. Our learning architecture jointly defines data in an Eulerian world space, using a static background grid, and a Lagrangian material space, using moving particles. By introducing this Eulerian-Lagrangian representation, we are able to naturally evolve and accumulate particle features using flow velocities generated from a generalized, high-dimensional force field. We demonstrate the efficacy of this system by solving various point cloud classification and segmentation problems with state-of-the-art performance. The entire geometric reservoir and data flow mimics the pipeline of the classic PIC/FLIP scheme in modeling natural flow, bridging the disciplines of geometric machine learning and physical simulation.
