PointCaps: Raw Point Cloud Processing using Capsule Networks with Euclidean Distance Routing

Raw point cloud processing using capsule networks is widely adopted in classification, reconstruction, and segmentation due to its ability to preserve spatial agreement of the input data. However, most of the existing capsule based network approaches are computationally heavy and fail at representin...

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Bibliographic Details
Published in:arXiv.org 2022-08
Main Authors: Denipitiyage, Dishanika, Jayasundara, Vinoj, Ranga Rodrigo, Chamira U S Edussooriya
Format: Article
Language:English
Subjects:
Algorithms
Classification
Cloud computing
Euclidean geometry
Image segmentation
Networks
Parameters
Reconstruction
Representations
Online Access:Get full text
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Summary:Raw point cloud processing using capsule networks is widely adopted in classification, reconstruction, and segmentation due to its ability to preserve spatial agreement of the input data. However, most of the existing capsule based network approaches are computationally heavy and fail at representing the entire point cloud as a single capsule. We address these limitations in existing capsule network based approaches by proposing PointCaps, a novel convolutional capsule architecture with parameter sharing. Along with PointCaps, we propose a novel Euclidean distance routing algorithm and a class-independent latent representation. The latent representation captures physically interpretable geometric parameters of the point cloud, with dynamic Euclidean routing, PointCaps well-represents the spatial (point-to-part) relationships of points. PointCaps has a significantly lower number of parameters and requires a significantly lower number of FLOPs while achieving better reconstruction with comparable classification and segmentation accuracy for raw point clouds compared to state-of-the-art capsule networks.
ISSN:2331-8422