Transcription of Multi-view Convolutional Neural Networks for 3D Shape ...
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Multi-view Convolutional Neural Networks for 3D Shape ...
Multi-view Convolutional Neural Networks for 3D Shape RecognitionHang SuSubhransu MajiEvangelos KalogerakisErik Learned-MillerUniversity of Massachusetts, longstanding question in computer vision concerns therepresentation of 3D shapes for recognition: should 3 Dshapes be represented with descriptors operating on theirnative 3D formats, such as voxel grid or polygon mesh, orcan they be effectively represented with view -based descrip-tors? We address this question in the context of learningto recognize 3D shapes from a collection of their renderedviews on 2D images.
sketches with line drawings of 3D models produced from several different views based on local Gabor filters, while Schneider et al. [30] proposed using Fisher vectors [26] on SIFT features [22] for representing human sketches of shapes. These descriptors are largely “hand-engineered” and some do not generalize well across different domains.
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