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Raw point cloud processing using capsule networks is widely adopted inclassification, reconstruction, and segmentation due to its ability to preservespatial agreement of the input data. However, most of the existing capsulebased network approaches are computationally heavy and fail at representing theentire point cloud as a single capsule. We address these limitations inexisting capsule network based approaches by proposing PointCaps, a novelconvolutional capsule architecture with parameter sharing. Along withPointCaps, we propose a novel Euclidean distance routing algorithm and aclass-independent latent representation. The latent representation capturesphysically interpretable geometric parameters of the point cloud, with dynamicEuclidean routing, PointCaps well-represents the spatial (point-to-part)relationships of points. PointCaps has a significantly lower number ofparameters and requires a significantly lower number of FLOPs while achievingbetter reconstruction with comparable classification and segmentation accuracyfor raw point clouds compared to state-of-the-art capsule networks.

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