3D Object Classification Based on Multi Convolutional Neural Networks

Mei-qi LU, Wei LI, Ya-guang NING


With the development of 3D sensing technology in recent years, making it possible to obtain high quality color image as well as its depth information, the combination of both can improve the accuracy of image classification. Traditional methods are mostly based on well-designed features separately for color image or depth image, which does not consider relation-features between color and depth information. A 3D object classification approach based on multi convolutional neural networks is presented in this paper. Our approach consists of three parts: (1) Two resizing methods for keeping image aspect ratio were proposed, which can keep the aspect ratio information of the original image, besides, data was augmented by applying both methods. (2) Different from traditional methods, CNN (Convolutional Neural Network) was used to extract features from both color image and depth image. (3) Consider on the relation-features about color and depth, we designed a multi CNN which focuses on relation-features and trained it successful, that is, we could extract relation-features using this network. Comparing with current approaches, our experiment results show better performance on 3D object classification.


3D object classification, Convolutional neural network, Image classification


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