3D shape recognition has drawn much attention in recent years.The view-based approach performs best of all.However,the current multi-view methods are almost all fully supervised,and the pretraining models are almost a...3D shape recognition has drawn much attention in recent years.The view-based approach performs best of all.However,the current multi-view methods are almost all fully supervised,and the pretraining models are almost all based on ImageNet.Although the pretraining results of ImageNet are quite impressive,there is still a significant discrepancy between multi-view datasets and ImageNet.Multi-view datasets naturally retain rich 3D information.In addition,large-scale datasets such as ImageNet require considerable cleaning and annotation work,so it is difficult to regenerate a second dataset.In contrast,unsupervised learning methods can learn general feature representations without any extra annotation.To this end,we propose a three-stage unsupervised joint pretraining model.Specifically,we decouple the final representations into three fine-grained representations.Data augmentation is utilized to obtain pixel-level representations within each view.And we boost the spatial invariant features from the view level.Finally,we exploit global information at the shape level through a novel extract-and-swap module.Experimental results demonstrate that the proposed method gains significantly in 3D object classification and retrieval tasks,and shows generalization to cross-dataset tasks.展开更多
基金This work was supported in part by National Natural Science Foundation of China(No.61976095)the Science and Technology Planning Project of Guangdong Province,China(No.2018B030323026).
文摘3D shape recognition has drawn much attention in recent years.The view-based approach performs best of all.However,the current multi-view methods are almost all fully supervised,and the pretraining models are almost all based on ImageNet.Although the pretraining results of ImageNet are quite impressive,there is still a significant discrepancy between multi-view datasets and ImageNet.Multi-view datasets naturally retain rich 3D information.In addition,large-scale datasets such as ImageNet require considerable cleaning and annotation work,so it is difficult to regenerate a second dataset.In contrast,unsupervised learning methods can learn general feature representations without any extra annotation.To this end,we propose a three-stage unsupervised joint pretraining model.Specifically,we decouple the final representations into three fine-grained representations.Data augmentation is utilized to obtain pixel-level representations within each view.And we boost the spatial invariant features from the view level.Finally,we exploit global information at the shape level through a novel extract-and-swap module.Experimental results demonstrate that the proposed method gains significantly in 3D object classification and retrieval tasks,and shows generalization to cross-dataset tasks.
