We have developed a neural network-based computer vision method for detection
of storage space within storage furniture. The method consists of automatic storage
volume detection and annotation within 3D models of furniture, and automatic
generation of a large number of depth images of storage furniture with assigned
bounding boxes representing the storage space above the furniture shelves. These
scenes are used for the training of a neural network. The proposed method enables
storage space detection in depth images acquired by a real 3D camera. Depth images
with annotations of storage space bounding boxes are also a contribution of this paper
and are available for further research. The proposed approach represents a novel research
topic, and the results show that it is possible to facilitate a network originally developed for
object detection to detect empty or cluttered storage volumes.
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Hržica, Mateja; Pejić, Petra; Hartmann Tolić, Ivana; Cupec, Robert
Detection of Household Furniture Storage Space in Depth Images //
Sensors, 22(18) (2022), 6774, 29 doi:10.3390/s22186774
https://www.mdpi.com/1424-8220/22/18/6774#
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