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We study the problem of how to build a deep learning representation for 3Dshape. Deep learning has shown to be very effective in variety of visualapplications, such as image classification and object detection. However, ithas not been successfully applied to 3D shape recognition. This is because 3Dshape has complex structure in 3D space and there are limited number of 3Dshapes for feature learning. To address these problems, we project 3D shapesinto 2D space and use autoencoder for feature learning on the 2D images. Highaccuracy 3D shape retrieval performance is obtained by aggregating the featureslearned on 2D images. In addition, we show the proposed deep learning featureis complementary to conventional local image descriptors. By combing the globaldeep learning representation and the local descriptor representation, ourmethod can obtain the state-of-the-art performance on 3D shape retrievalbenchmarks.

Deep Learning Representation using Autoencoder for 3D Shape Retrieval