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Learning 3D Shapes as Multi-layered Height-Maps Using 2D Convolutional Networks

  • Kripasindhu Sarkar
  • Basavaraj Hampiholi
  • Kiran Varanasi
  • Didier Stricker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11220)

Abstract

We present a novel global representation of 3D shapes, suitable for the application of 2D CNNs. We represent 3D shapes as multi-layered height-maps (MLH) where at each grid location, we store multiple instances of height maps, thereby representing 3D shape detail that is hidden behind several layers of occlusion. We provide a novel view merging method for combining view dependent information (Eg. MLH descriptors) from multiple views. Because of the ability of using 2D CNNs, our method is highly memory efficient in terms of input resolution compared to the voxel based input. Together with MLH descriptors and our multi view merging, we achieve the state-of-the-art result in classification on ModelNet dataset.

Keywords

CNN on 3D shapes 3D shape representation ModelNet Shape classification Shape generation 

Notes

Acknowledgements

This work was partially funded by the BMBF project DYNAMICS (01IW15003).

Supplementary material

474218_1_En_5_MOESM1_ESM.pdf (921 kb)
Supplementary material 1 (pdf 920 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Kripasindhu Sarkar
    • 1
    • 2
  • Basavaraj Hampiholi
    • 2
  • Kiran Varanasi
    • 1
  • Didier Stricker
    • 1
    • 2
  1. 1.DFKI KaiserslauternKaiserslauternGermany
  2. 2.Technische Universität KaiserslauternKaiserslauternGermany

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