Consumer-grade range cameras such as the Kinect sensor have the potential to be used in mapping applications where accuracy requirements are less strict. To realize this potential insight into the geometric quality of the data acquired by the sensor is essential. In this paper we discuss the calibration of the Kinect sensor, and provide an analysis of the accuracy and resolution of its depth data. Based on a mathematical model of depth measurement from disparity a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimeters up to about 4 cm at the maximum range of the sensor. The quality of the data is also found to be influenced by the low resolution of the depth measurements.

With the increasing point densities provided by airborne laser scanner (ALS) data the requirements on derived products also increase. One major application of ALS data is to provide input for 3D city models. Modeling of roof faces, (3D) road and terrain surfaces can partially be done in an automated manner, although many such approaches are still in a development stage. Problems in automatic building reconstruction lie in the dynamic area between assumptions and reality. Not every object in the data appears as the algorithm expects. Challenges are to detect areas that cannot be reconstructed automatically. This paper describes our contribution to the field of building reconstruction by proposing a target based graph matching approach that can handle both complete and incomplete laser data. Match results describe which target objects appear topologically in the data. Complete match results can be reconstructed in an automated manner. Quality parameters store information on how the model fits to the input data and which data has not been used. Areas where laser data only partly matches with target objects are detected automatically. Four datasets are analyzed in order to describe the quality of the automatically reconstructed roofs, and to point out the reasons why segments are left out from the automatic reconstruction. The reasons why these areas are left out include lack of data information and limitations of our initial target objects. Potential improvement to our approach is to include likelihood functions to the existence of topological relations.