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A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors? We address this question in the context of learning to recognize 3D shapes from a collection of their rendered views on 2D images. We first present a standard CNN architecture trained to recognize thedoi:10.1109/iccv.2015.114 dblp:conf/iccv/SuMKL15 fatcat:4gyiniflprhj3pxu466yild5ku