Abstract
The combination of data coming from multiple sensors is more and more applied for remote sensing issues (multi-sensor imagery) but also in cultural heritage or robotics, since it often results in increased robustness and accuracy of the final data. In this paper, the reconstruction of building elements such as window frames or door jambs scanned thanks to a low cost 3D sensor (Kinect v2) is presented. Their combination within a global point cloud of an indoor scene acquired with a terrestrial laser scanner (TLS) is considered. If the added elements acquired with the Kinect sensor enable to reach a better level of detail of the final model, an adapted acquisition protocol may also provide several benefits as for example time gain. The paper aims at analyzing whether the two measurement techniques can be complementary in this context. The limitations encountered during the acquisition and reconstruction steps are also investigated.
Highlights
Combining data from various sensors is a wide but promising topic
This paper proposes an original combination of 3D data obtained with a Kinect v2 sensor, with a global terrestrial laser scanners (TLS) point cloud
The way TLS and Kinect data have been registered has an influence on the final model quality, since both of them are used for different purposes during the model reconstruction
Summary
Combining data from various sensors is a wide but promising topic. Next to additional computations implied because of heterogeneous data handling, it enables to overcome the weaknesses of a kind of device thanks to the strengths of another one. As a matter of fact, laser scanning technologies enable to obtain a large amount of accurate 3D data. Despite these benefits, occlusions may occur in the produced point clouds because of the geometry of the scene, restricting the automation of the modeling process. The acquisition process can be very time-consuming if a high level of detail (LoD) is required. To improve these aspects, this paper proposes an original combination of 3D data obtained with a Kinect v2 sensor, with a global TLS point cloud. It will be interesting to analyze whether these geometrical primitives acquired with Kinect sensor can contribute to a better LoD of the final model
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