AdvectiveNet：一个用于点云处理的欧拉-拉格朗日流动储集层
AdvectiveNet: An Eulerian-Lagrangian Fluidic reservoir for Point Cloud Processing
摘要：
本文提出了一种新的物理学启发的深度学习方法，用于点云处理，其动机是流体力学中的自然流动现象。我们的学习架构使用静态背景网格在欧拉世界空间中共同定义数据，使用移动粒子在拉格朗日材料空间中定义数据。通过引入这种欧拉-拉格朗日表示，我们能够使用从广义高维力场产生的流速自然地演化和积累粒子特征。我们通过解决各种点云分类和分割问题，展示了该系统的有效性。整个几何库和数据流模拟了经典PIC/FLIP方案在建模自然流时的管道，桥接了几何机器学习和物理仿真的学科。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
