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Learning objects from RGB-D sensors using point cloud-based neural networks

In this paper we present a scene understanding approach for assistive robotics based on learning to recognize different objects from RGB-D devices. Using the depth information it is possible to compute descriptors that capture the geometrical relations among the points that constitute an object or extract features from multiple viewpoints. We developed a framework for testing different neural models that receive this depth information as input. Also, we propose a novel approach using three-dimensional RGB-D information as input to Convolutional Neural Networks. We found F1-scores greater than 0.9 for the majority of the objects tested, showing that the adopted approach is effective as well for classification.