AdvectiveNet: An Eulerian-Lagrangian Fluidic reservoir for Point Cloud Processing

• URL: http://arxiv.org/abs/2002.00118v3
• Date: Wed, 24 Jun 2020 19:44:09 GMT
• Title: AdvectiveNet: An Eulerian-Lagrangian Fluidic reservoir for Point Cloud Processing
• Authors: Xingzhe He, Helen Lu Cao, Bo Zhu
• Abstract summary: This paper presents a 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. We demonstrate the efficacy of this system by solving various point cloud classification and segmentation problems with state-of-the-art performance.
• Score: 14.160687527074858
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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