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This paper proposes a single-shot approach for recognising clothingcategories from 2.5D features. We propose two visual features, BSP (B-SplinePatch) and TSD (Topology Spatial Distances) for this task. The local BSPfeatures are encoded by LLC (Locality-constrained Linear Coding) and fused withthree different global features. Our visual feature is robust to deformableshapes and our approach is able to recognise the category of unknown clothingin unconstrained and random configurations. We integrated the categoryrecognition pipeline with a stereo vision system, clothing instance detection,and dual-arm manipulators to achieve an autonomous sorting system. To verifythe performance of our proposed method, we build a high-resolution RGBDclothing dataset of 50 clothing items of 5 categories sampled in randomconfigurations (a total of 2,100 clothing samples). Experimental results showthat our approach is able to reach 83.2\% accuracy while classifying clothingitems which were previously unseen during training. This advances beyond theprevious state-of-the-art by 36.2\%. Finally, we evaluate the proposed approachin an autonomous robot sorting system, in which the robot recognises a clothingitem from an unconstrained pile, grasps it, and sorts it into a box accordingto its category. Our proposed sorting system achieves reasonable sortingsuccess rates with single-shot perception.

Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting