ABSTRACT
The extraction of permanent structures (such as walls, floors, and ceilings) is an important step in the reconstruction of building interiors from point clouds. These permanent structures are, in general, assumed to be planar. However, point clouds from building interiors often also contain clutter with planar surfaces such as furniture, cabinets, etc. Hence, not all planar surfaces that are extracted belong to permanent structures. This is undesirable as it can result in geometric errors in the reconstruction. Therefore, it is important that reconstruction methods can correctly detect and extract all permanent structures even in the presence of such clutter. We propose to perform semantic scene completion using deep learning, prior to the extraction of permanent structures to improve the reconstruction results. For this, we started from the ScanComplete network proposed by Dai et al. We adapted the network to use a different input representation to eliminate the need for scanning trajectory information as this is not always available. Furthermore, we optimized the architecture to make inference and training significantly faster. To further improve the results of the network, we created a more realistic dataset based on real-life scans from building interiors. The experimental results show that our approach significantly improves the extraction of the permanent structures from both synthetically generated as well as real-life point clouds, thereby improving the overall reconstruction results.
ABSTRACT
Due to the rapidly aging population, developing automated home care systems is a very important step in taking care of elderly people. This will enable us to automatically monitor the health of senior citizens in their own living environment and prevent problems before they happen. One of the challenging tasks is to actively monitor walking habits of elderlies, who alternate between the use of different walking aids, and to combine this with automated fall risk assessment systems. We propose a camera based system that uses object categorization techniques to robustly detect walking aids, like a walker, in order to improve the classification of the fall risk. By automatically integrating the application specific scenery knowledge like camera position and used walker type, we succeed in detecting walking aids within a single frame with an accuracy of 68% for trajectory A and 38% for trajectory B. Furthermore, compared to current state of the art detection systems, we use a rather limited set of training data to achieve this accuracy and thus create a system that is easily adaptable for other applications. Moreover, we applied spatial constraints between detections to optimize the object detection output and to limit the amount of false positive detections. Finally, we evaluate the output on a walking sequence base, leading up to a 92.3% correct classification rate of walking sequences. It can be noted that adapting this approach to other walking aids, like a walking cane, is quite straightforward and opens up the door for many future applications.