# Detection of Household Furniture Storage Space in Depth Images
## 2 Related Research
### On-Shelf Availability and Object Placement  
The problem of determining whether a product is available or out of stock on a retail shelf (OOS) is called on-shelf availability (OSA). In [23], the retail product detection problem is decomposed into a detection phase and a recognition phase. They segment the image into product and non-product segments and propose a method for determining shelf boundaries. Based on the peaks of a sample histogram, the segmentation of the shelves becomes clear to the human eye. They refer to the peaks as the positions of the products and their midpoints as the shelf boundaries. A 3D vision-based shelf monitoring (3D-VSM) system which automatically estimates OSA of products in a retail is proposed in [24; 25]. In the proposed method, the reference model of the shelf is compared with a current depth image of the shelf, and alerts for OOS events are generated without a priori knowledge of the product type. This system was experimentally used in a retail store, where it accurately estimated OSA of the products. Another recent approach for determination of the OSA is given in [26]. The authors propose a machine learning pipeline for real-time empty shelf detection in 2D images for the purposes of retail performance improvement. Automatic detection of empty space between displayed products in retail on 2D images is proposed in [27]. The authors also provide an annotated dataset. In [28], a supervised learning approach is proposed for detecting out-of-stock items using texture, color, and geometry features in high-resolution panoramic images of supermarket shelves. Using images from a surveillance camera, a method for robust shelf monitoring through detection and classification is proposed in [29]. The method presented in [30] learns how to arrange objects to organize a shelf or sort objects in boxes according to users' preferences. The main idea is that the robot has to assign the objects on the table to the shelves according to the predicted preferences of the user. However, this method does not calculate the available space on the shelves or in the boxes, but focuses more on assigning an object to the correct shelf. The above algorithms use 2D objects or images without depth to detect and identify shelves. In our work, we studied the detection of shelves, tables, or other furniture in depth images. Our method can also be used for a more general purpose, namely, to find storage space in previously unseen furniture, and it provides information about storage capacity whether it is currently occupied or not.  
The result of our method could possibly be used with the planning algorithms proposed in [6; 31]. A push planning algorithm proposed in [6] selects a sequence of actions to create space for the placement of objects. The objects have a shape, position, and orientation in 2D. The height of an object and the height of the storage volume are not considered. On the contrary, in the scene generation algorithm proposed in our paper, the height of the storage space bounding box and the heights of the objects are taken into account when placing objects on the shelves, that is, when creating clutter, and in the collision avoidance of objects in 3D. A data-driven method described in [31] was developed for arranging objects that automatically fill empty spaces on shelves with a single exampler to create arrangements. Their method creates a variety of style preserving arrangements that scale to different cabinet sizes and confirm style preservation by the user. Our algorithm could be used to detect storage volumes to which the referenced object placement method could be applied.