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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation

verfasst von : Marie-Julie Rakotosaona, Maks Ovsjanikov

Erschienen in: Computer Vision – ECCV 2020

Verlag: Springer International Publishing

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Abstract

We present a learning-based method for interpolating and manipulating 3D shapes represented as point clouds, that is explicitly designed to preserve intrinsic shape properties. Our approach is based on constructing a dual encoding space that enables shape synthesis and, at the same time, provides links to the intrinsic shape information, which is typically not available on point cloud data. Our method works in a single pass and avoids expensive optimization, employed by existing techniques. Furthermore, the strong regularization provided by our dual latent space approach also helps to improve shape recovery in challenging settings from noisy point clouds across different datasets. Extensive experiments show that our method results in more realistic and smoother interpolations compared to baselines. Both the code and our pre-trained network can be found online: https://​github.​com/​mrakotosaon/​intrinsic_​interpolations.

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Metadaten
Titel
Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation
verfasst von
Marie-Julie Rakotosaona
Maks Ovsjanikov
Copyright-Jahr
2020
Buch
Computer Vision – ECCV 2020

Print ISBN: 978-3-030-58535-8

Electronic ISBN: 978-3-030-58536-5

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-58536-5_39

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