From depth image to semantic scene synthesis through point cloud classification and labeling: Application to assistive systems
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The aim of this work is to provide a semantic scene synthesis from depth image. First, depth image is segmented and each segment is classified in the context of assistive systems using a deep learning network. Second, inspired by the Braille system and the Japanese writing system Kanji, the obtained classes are coded with semantic labels. A semantic scene is then synthesized using the labels and extracted features. Experiments are conducted on noisy, occluded, cropped and incomplete data including acquired depth images of indoor scenes and datasets from the RMRC challenge. The obtained results are reported and discussed.
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