NormalNet: A voxel-based CNN for 3D object classification and retrieval

Cheng Wang, Ming Cheng, Ferdous Sohel, Mohammed Bennamoun, Jonathan Li

Research output: Contribution to journalArticle

Abstract

A common approach to tackle 3D object recognition tasks is to project 3D data to multiple 2D images. Projection only captures the outline of the object, and discards the internal information that may be crucial for the recognition. In this paper, we stay in 3D and concentrate on tapping the potential of 3D representations. We present NormalNet, a voxel-based convolutional neural network (CNN) designed for 3D object recognition. The network uses normal vectors of the object surfaces as input, which demonstrate stronger discrimination capability than binary voxels. We propose a reflection-convolution-concatenation (RCC) module to realize the cony layers, which extracts distinguishable features for 3D vision tasks while reducing the number of parameters significantly. We further improve the performance of NormalNet by combining two networks, which take normal vectors and voxels as input respectively. We carry out a series of experiments that validate the design of the network and achieve competitive performance in 3D object classification and retrieval tasks. (C) 2018 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)139-147
Number of pages9
JournalNeurocomputing
Volume323
DOIs
Publication statusPublished - 5 Jan 2019

Cite this

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title = "NormalNet: A voxel-based CNN for 3D object classification and retrieval",
abstract = "A common approach to tackle 3D object recognition tasks is to project 3D data to multiple 2D images. Projection only captures the outline of the object, and discards the internal information that may be crucial for the recognition. In this paper, we stay in 3D and concentrate on tapping the potential of 3D representations. We present NormalNet, a voxel-based convolutional neural network (CNN) designed for 3D object recognition. The network uses normal vectors of the object surfaces as input, which demonstrate stronger discrimination capability than binary voxels. We propose a reflection-convolution-concatenation (RCC) module to realize the cony layers, which extracts distinguishable features for 3D vision tasks while reducing the number of parameters significantly. We further improve the performance of NormalNet by combining two networks, which take normal vectors and voxels as input respectively. We carry out a series of experiments that validate the design of the network and achieve competitive performance in 3D object classification and retrieval tasks. (C) 2018 Elsevier B.V. All rights reserved.",
keywords = "3D object classification, 3D object retrieval, Convolutional neural network, Network fusion",
author = "Cheng Wang and Ming Cheng and Ferdous Sohel and Mohammed Bennamoun and Jonathan Li",
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NormalNet : A voxel-based CNN for 3D object classification and retrieval. / Wang, Cheng; Cheng, Ming; Sohel, Ferdous; Bennamoun, Mohammed; Li, Jonathan.

In: Neurocomputing, Vol. 323, 05.01.2019, p. 139-147.

Research output: Contribution to journalArticle

TY - JOUR

T1 - NormalNet

T2 - A voxel-based CNN for 3D object classification and retrieval

AU - Wang, Cheng

AU - Cheng, Ming

AU - Sohel, Ferdous

AU - Bennamoun, Mohammed

AU - Li, Jonathan

PY - 2019/1/5

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AB - A common approach to tackle 3D object recognition tasks is to project 3D data to multiple 2D images. Projection only captures the outline of the object, and discards the internal information that may be crucial for the recognition. In this paper, we stay in 3D and concentrate on tapping the potential of 3D representations. We present NormalNet, a voxel-based convolutional neural network (CNN) designed for 3D object recognition. The network uses normal vectors of the object surfaces as input, which demonstrate stronger discrimination capability than binary voxels. We propose a reflection-convolution-concatenation (RCC) module to realize the cony layers, which extracts distinguishable features for 3D vision tasks while reducing the number of parameters significantly. We further improve the performance of NormalNet by combining two networks, which take normal vectors and voxels as input respectively. We carry out a series of experiments that validate the design of the network and achieve competitive performance in 3D object classification and retrieval tasks. (C) 2018 Elsevier B.V. All rights reserved.

KW - 3D object classification

KW - 3D object retrieval

KW - Convolutional neural network

KW - Network fusion

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