We present an algorithm that simultaneously calibrates two color cameras, a depth camera, and the relative pose between them. The method is designed to have three key features: accurate, practical, and applicable to a wide range of sensors. The method requires only a planar surface to be imaged from various poses. The calibration does not use depth discontinuities in the depth image, which makes it flexible and robust to noise. We apply this calibration to a Kinect device and present a new depth distortion model for the depth sensor. We perform experiments that show an improved accuracy with respect to the manufacturer's calibration.

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Abstract. Many 3D reconstruction methods produce incomplete depth maps. Depth map inpainting can generate visually plausible structures for the missing areas. We present an inpainting method that encourages flat surfaces without favouring fronto-parallel planes. Moreover, it uses a color image to guide the inpainting and align color and depth edges. We implement the algorithm efficiently through graph-cuts. We compare the performance of our method with another inpainting approach used for large datasets and we show the results using several datasets. The depths inpainted with our method are visually plausible and of higher quality.

Abstract. Combining long sequences of overlapping depth maps without simplification results in a huge number of redundant points, which slows down further processing. In this paper, a novel method is presented for incrementally creating a nonredundant point cloud with varying levels of detail without limiting the captured volume or requiring any parameters from the user. Overlapping measurements are used to refine point estimates by reducing their directional variance. The algorithm was evaluated with plane and cube fitting residuals, which were improved considerably over redundant point clouds.

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