3DPointCaps++: Learning 3D Representations with Capsule Networks

We present 3DPointCaps++ for learning robust, flexible and generalizable 3D object representations without requiring heavy annotation efforts or supervision. Unlike conventional 3D generative models, our algorithm aims for building a structured latent space where certain factors of shape variations,...

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Vydané v:	International journal of computer vision Ročník 130; číslo 9; s. 2321 - 2336
Hlavní autori:	Zhao, Yongheng, Fang, Guangchi, Guo, Yulan, Guibas, Leonidas, Tombari, Federico, Birdal, Tolga
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.09.2022 Springer Springer Nature B.V
Predmet:	Algorithms Annotations Artificial Intelligence Computer Imaging Computer Science Image Processing and Computer Vision Interpolation Learning Pattern Recognition Pattern Recognition and Graphics Representations Robustness Segmentation Special Issue on 3D Computer Vision Three dimensional models Vision Representation learning 3D Shapes 3D Reconstruction Autoencoder 3D Point clouds Capsule networks Unsupervised learning
ISSN:	0920-5691, 1573-1405
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Popis
Shrnutí:	We present 3DPointCaps++ for learning robust, flexible and generalizable 3D object representations without requiring heavy annotation efforts or supervision. Unlike conventional 3D generative models, our algorithm aims for building a structured latent space where certain factors of shape variations, such as object parts, can be disentangled into independent sub-spaces. Our novel decoder then acts on these individual latent sub-spaces (i.e. capsules) using deconvolution operators to reconstruct 3D points in a self-supervised manner. We further introduce a cluster loss ensuring that the points reconstructed by a single capsule remain local and do not spread across the object uncontrollably. These contributions allow our network to tackle the challenging tasks of part segmentation, part interpolation/replacement as well as correspondence estimation across rigid / non-rigid shape, and across / within category. Our extensive evaluations on ShapeNet objects and human scans demonstrate that our network can learn generic representations that are robust and useful in many applications.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Communicated by Akihiro Sugimoto.
ISSN:	0920-5691 1573-1405
DOI:	10.1007/s11263-022-01632-6