Search
Publication Authors

Prof. Dr. Didier Stricker

Dr. Alain Pagani

Dr. Gerd Reis

Eric Thil

Keonna Cunningham

Dr. Oliver Wasenmüller

Dr. Muhammad Zeshan Afzal

Dr. Gabriele Bleser

Dr. Muhammad Jameel Nawaz Malik
Dr. Bruno Mirbach

Dr. Jason Raphael Rambach

Dr. Nadia Robertini

Dr. René Schuster

Dr. Bertram Taetz

Ahmed Aboukhadra

Sk Aziz Ali

Mhd Rashed Al Koutayni

Yuriy Anisimov

Jilliam Maria Diaz Barros

Ramy Battrawy
Hammad Butt

Mahdi Chamseddine

Steve Dias da Cruz
Fangwen Shu

Torben Fetzer

Ahmet Firintepe

Sophie Folawiyo

David Michael Fürst
Anshu Garg

Christiano Couto Gava
Suresh Guttikonda

Tewodros Amberbir Habtegebrial

Simon Häring

Khurram Azeem Hashmi
Henri Hoyez

Alireza Javanmardi

Jigyasa Singh Katrolia

Andreas Kölsch
Ganesh Shrinivas Koparde
Onorina Kovalenko

Stephan Krauß
Paul Lesur

Michael Lorenz

Dr. Markus Miezal

Mina Ameli

Nareg Minaskan Karabid

Mohammad Minouei

Pramod Murthy

Mathias Musahl

Peter Neigel

Manthan Pancholi

Mariia Podguzova

Praveen Nathan
Qinzhuan Qian
Rishav
Marcel Rogge
María Alejandra Sánchez Marín
Dr. Kripasindhu Sarkar

Alexander Schäfer

Pascal Schneider

Mohamed Selim

Tahira Shehzadi
Lukas Stefan Staecker

Yongzhi Su

Xiaoying Tan
Christian Witte

Yaxu Xie

Vemburaj Yadav

Dr. Vladislav Golyanik

Dr. Aditya Tewari

André Luiz Brandão
Publication Archive
New title
- ActivityPlus
- AlterEgo
- AR-Handbook
- ARVIDA
- Auroras
- AVILUSplus
- Be-greifen
- Body Analyzer
- CAPTURE
- Co2Team
- COGNITO
- DAKARA
- Density
- DYNAMICS
- EASY-IMP
- ENNOS
- Eyes Of Things
- iACT
- IMCVO
- IVMT
- LARA
- LiSA
- Marmorbild
- Micro-Dress
- Odysseus Studio
- On Eye
- OrcaM
- PAMAP
- PROWILAN
- ServiceFactory
- STREET3D
- SUDPLAN
- SwarmTrack
- TuBUs-Pro
- VIDETE
- VIDP
- VisIMon
- VISTRA
- VIZTA
- You in 3D
Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks

Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks
Kripasindhu Sarkar, Basavaraj Hampiholi, Kiran Varanasi, Didier Stricker
Computer Vision -- ECCV 2018 European Conference on Computer Vision (ECCV-2018), September 8-14, Munich, Germany
- 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.
Other details
Please find the -
- Keywords:
- cnn, shape representation, multiview architecture