Learning-Based Multiple Pooling Fusion in Multi-View Convolutional Neural Network for 3D Model Classification and Retrieval
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초록
We design an ingenious view-pooling method named learning-based multiple pooling fusion (LMPF), andapply it to multi-view convolutional neural network (MVCNN) for 3D model classification or retrieval. By thismeans, multi-view feature maps projected from a 3D model can be compiled as a simple and effective featuredescriptor. The LMPF method fuses the max pooling method and the mean pooling method by learning a setof optimal weights. Compared with the hand-crafted approaches such as max pooling and mean pooling, theLMPF method can decrease the information loss effectively because of its “learning” ability. Experiments onModelNet40 dataset and McGill dataset are presented and the results verify that LMPF can outperform thoseprevious methods to a great extent.
Abstract
We design an ingenious view-pooling method named learning-based multiple pooling fusion (LMPF), andapply it to multi-view convolutional neural network (MVCNN) for 3D model classification or retrieval. By thismeans, multi-view feature maps projected from a 3D model can be compiled as a simple and effective featuredescriptor. The LMPF method fuses the max pooling method and the mean pooling method by learning a setof optimal weights. Compared with the hand-crafted approaches such as max pooling and mean pooling, theLMPF method can decrease the information loss effectively because of its “learning” ability. Experiments onModelNet40 dataset and McGill dataset are presented and the results verify that LMPF can outperform thoseprevious methods to a great extent.
참고문헌 (31)
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