Spline interpolation is widely used in many different applications like computer graphics, animations and robotics. Many of these applications are run in real-time with constraints on computational complexity, thus fueling the need for computational inexpensive, real-time, continuous and loop-free data interpolation techniques. Often Catmull-Rom splines are used, which use four control-points: the two points between which to interpolate as well as the point directly before and the one directly after. If interpolating over time, this last point will ly in the future. However, in real-time applications future values may not be known in advance, meaning that Catmull-Rom splines are not applicable. In this paper we introduce another family of interpolation splines (dubbed Three-Point-Splines) which show the same characteristics as Catmull-Rom, but which use only three control-points, omitting the one "in the future". Therefore they can generate smooth interpolation curves even in applications which do not have knowledge of future points, without the need for more computational complex methods. The generated curves are more rigid than Catmull-Rom, and because of that the Three-Point-Splines will not generate self-intersections within an interpolated curve segment, a property that has to be introduced to Catmull-Rom by careful parameterization. Thus, the Three-Point-Splines allow for greater freedom in parameterization, and can therefore be adapted to the application at hand, e.g. to a requested curvature or limitations on acceleration/deceleration. We will also show a method that allows to change the control-points during an ongoing interpolation, both with Thee-Point-Splines as well as with Catmull-Rom splines.

Abstract

More and more devices have depth sensors, making RGB+D video (colour+depth video) increasingly common. RGB+D video allows the use of depth image based rendering (DIBR) to render a given scene from different viewpoints, thus making it a useful asset in view prediction for 3D and free-viewpoint video coding. In this paper we evaluate a multitude of algorithms for scattered data interpolation, in order to optimize the performance of DIBR for video coding. This also includes novel contributions like a Kriging refinement step, an edge suppression step to suppress artifacts, and a scale-adaptive kernel. Our evaluation uses the depth extension of the Sintel datasets. Using ground-truth sequences is crucial for such an optimization, as it ensures that all errors and artifacts are caused by the prediction itself rather than noisy or erroneous data. We also present a comparison with the commonly used mesh-based projection.

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