Abstract. In this paper, we present a framework for range flow estimation from Microsoft's multi-modal imaging device Kinect. We address all essential stages of the flow computation process, starting from the calibration of the Kinect, over the alignment of the range and color channels, to the introduction of a novel multi-modal range flow algorithm which is robust against typical (technology dependent) range estimation artifacts.

The Multipath effect in Time-of-Flight(ToF) cameras still remains to be a challenging problem that hinders further processing of 3D data information. Based on the evidence from previous literature, we explored the possibility of using machine learning techniques to correct this effect. Firstly, we created two new datasets of of ToF images rendered via ToF simulator of LuxRender. These two datasets contain corners in multiple orientations and with different material properties. We chose scenes with corners as multipath effects are most pronounced in corners. Secondly, we used this dataset to construct a learning model to predict real valued corrections to the ToF data using Random Forests. We found out that in our smaller dataset we were able to predict real valued correction and improve the quality of depth images significantly by removing multipath bias. With our algorithm, we improved relative per-pixel error from average value of 19% to 3%. Additionally, variance of the error was lowered by an order of magnitude.

In this paper, we study motion artifacts that arise in Time-ofFlight imaging of dynamic scenes caused by the sequential nature of the raw image acquisition process used to compute the final depth image. Many methods for compensation of such errors have been proposed to date, but still lack a proper comparison. We bridge this gap by not only evaluating those methods, but also by providing implementations for all of them as a base-line to the community. By exchanging the calibration model necessary for these methods with a model closer to reality we were able to improve the results on all related methods without any loss of performance.

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