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. We first present a standard CNN ar-chitecture trained to recognize the shapes rendered viewsindependently of each other, and show that a 3D shapecan be recognized even from a single view at an accuracyfar higher than using state-of-the-art 3D Shape rates further increase when multiple views ofthe shapes are provided.

(or, aspect graphs), there is relatively little work on learning to combine the view-based descriptors for 3D shape recog-nition. Most methods resort to simple strategies such as per-forming exhaustive pairwise comparisons of descriptors ex-tracted from different views of each shape, or concatenating descriptors from ordered, consistent views.

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