FPNN: Field Probing Neural Networks for 3D Data

NeurIPS 2016 Yangyan LiSoeren PirkHao SuCharles R. QiLeonidas J. Guibas

Building discriminative representations for 3D data has been an important task in computer graphics and computer vision research. Convolutional Neural Networks (CNNs) have shown to operate on 2D images with great success for a variety of tasks... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
3D Object Recognition ModelNet40 FPNN (4-FCs + NF) Accuracy 88.4% # 2