3D object recognition is the task of recognising objects from 3D data.
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Experimental results on ModelNet10 and ModelNet40 datasets show that our MV-C3D technique can achieve outstanding performance with multi-view images which are captured from partial angles with less range.
Depth perception is a key component for autonomous systems that interact in the real world, such as delivery robots, warehouse robots, and self-driving cars.
Such a perspective enables us to study deep multi-view learning in the context of regularized network training, for which we present control experiments of benchmark image classification to show the efficacy of our proposed CorrReg.
In this study, we present an analysis of model-based ensemble learning for 3D point-cloud object classification and detection.
Service robots are expected to be more autonomous and efficiently work in human-centric environments.
Three-dimensional object recognition has recently achieved great progress thanks to the development of effective point cloud-based learning frameworks, such as PointNet and its extensions.
With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance.