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3D Point Capsule Networks

2018-12-27CVPR 2019Code Available0· sign in to hype

Yongheng Zhao, Tolga Birdal, Haowen Deng, Federico Tombari

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Abstract

In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our novel unified 3D auto-encoder formulation. Their dynamic routing scheme and the peculiar 2D latent space deployed by our approach bring in improvements for several common point cloud-related tasks, such as object classification, object reconstruction and part segmentation as substantiated by our extensive evaluations. Moreover, it enables new applications such as part interpolation and replacement.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
ModelNet403D-PointCapsNetClassification Accuracy89.3Unverified

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