NDLI: 3D Mesh Labeling via Deep Convolutional Neural Networks

Content Provider	ACM Digital Library
Author	Guo, Kan Zou, Dongqing Chen, Xiaowu
Copyright Year	2015
Abstract	This article presents a novel approach for 3D mesh labeling by using deep Convolutional Neural Networks (CNNs). Many previous methods on 3D mesh labeling achieve impressive performances by using predefined geometric features. However, the generalization abilities of such low-level features, which are heuristically designed to process specific meshes, are often insufficient to handle all types of meshes. To address this problem, we propose to learn a robust mesh representation that can adapt to various 3D meshes by using CNNs. In our approach, CNNs are first trained in a supervised manner by using a large pool of classical geometric features. In the training process, these low-level features are nonlinearly combined and hierarchically compressed to generate a compact and effective representation for each triangle on the mesh. Based on the trained CNNs and the mesh representations, a label vector is initialized for each triangle to indicate its probabilities of belonging to various object parts. Eventually, a graph-based mesh-labeling algorithm is adopted to optimize the labels of triangles by considering the label consistencies. Experimental results on several public benchmarks show that the proposed approach is robust for various 3D meshes, and outperforms state-of-the-art approaches as well as classic learning algorithms in recognizing mesh labels.
Starting Page	1
Ending Page	12
Page Count	12
File Format	PDF
ISSN	07300301
e-ISSN	15577368
DOI	10.1145/2835487
Volume Number	35
Issue Number	1
Journal	ACM Transactions on Graphics (TOG)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-12-29
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	3D mesh labeling Deep convolutional neural networks Geometry features
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Learning 3D mesh segmentation and labeling

Deep joint demosaicking and denoising

Learning visual similarity for product design with convolutional neural networks

A benchmark for 3D mesh segmentation

Robust Visual Tracking via Convolutional Networks Without Training.

Geometric compression through topological surgery

Geometry-guided progressive lossless 3D mesh coding with octree (OT) decomposition

Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks.

A deep learning framework for character motion synthesis and editing

3D Mesh Labeling via Deep Convolutional Neural Networks

Similar Documents

Learning 3D mesh segmentation and labeling

Deep joint demosaicking and denoising

Learning visual similarity for product design with convolutional neural networks

A benchmark for 3D mesh segmentation

Robust Visual Tracking via Convolutional Networks Without Training.

Geometric compression through topological surgery

Geometry-guided progressive lossless 3D mesh coding with octree (OT) decomposition

Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks.

A deep learning framework for character motion synthesis and editing

3D Mesh Labeling via Deep Convolutional Neural Networks