The paradigm of Convolutional Neural Network (CNN) has already shown its potential for many challenging applications of computer vision, such as image classification, object detection and action recognition. In this paper, the task of 3D model retrieval is addressed by exploiting such promising paradigm. However, 3D models are usually represented with a collection of orderless points, lines and surfaces in a three dimensional space, which makes it difficult to involve the operation of convolution, pooling, etc. Yet, we propose a practical and effective way for applying CNN to 3D model retrieval, by training the network with the depth projections of 3D model. This CNN is regarded as a generic feature extractor for depth image. With large amounts of training data, the learned feature, which is called Neural Shape Codes, can handle various deformation changes that exist in shape analysis. The reported experimental results on several 3D shape benchmark datasets show the superior performance of the proposed method.

Neural shape codes for 3D model retrieval

BAI, XIANG;ROLI, FABIO
2015

Abstract

The paradigm of Convolutional Neural Network (CNN) has already shown its potential for many challenging applications of computer vision, such as image classification, object detection and action recognition. In this paper, the task of 3D model retrieval is addressed by exploiting such promising paradigm. However, 3D models are usually represented with a collection of orderless points, lines and surfaces in a three dimensional space, which makes it difficult to involve the operation of convolution, pooling, etc. Yet, we propose a practical and effective way for applying CNN to 3D model retrieval, by training the network with the depth projections of 3D model. This CNN is regarded as a generic feature extractor for depth image. With large amounts of training data, the learned feature, which is called Neural Shape Codes, can handle various deformation changes that exist in shape analysis. The reported experimental results on several 3D shape benchmark datasets show the superior performance of the proposed method.
3D model; CNN; Depth view; Shape retrieval; Software; Artificial Intelligence; 1707; Signal Processing
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/176801
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