Confidence driven TGV fusion

We introduce a novel model for spatially varying variational data fusion, driven by point-wise confidence values. The proposed model allows for the joint estimation of the data and the confidence values based on the spatial coherence of the data. We discuss the main properties of the introduced model as well as suitable algorithms for estimating the solution of the corresponding biconvex minimization problem and their convergence. The performance of the proposed model is evaluated considering the problem of depth image fusion by using both synthetic and real data from publicly available datasets

Multi-Modality Depth Map Fusion using Primal-Dual Optimization

We present a novel fusion method that combines complementary 3D and 2D imaging techniques. Consider a Time-of-Flight sensor that acquires a dense depth map on a wide depth range but with a comparably small resolution. Complementary, a stereo sensor generates a disparity map in high resolution but with occlusions and outliers. In our method, we fuse depth data, and optionally also intensity data using a primal-dual optimization, with an energy functional that is designed to compensate for missing parts, filter strong outliers and reduce the acquisition noise. The numerical algorithm is efficiently implemented on a GPU to achieve a processing speed of 10 to 15 frames per second. Experiments on synthetic, real and benchmark datasets show that the results are superior compared to each sensor alone and to competing optimization techniques. In a practical example, we are able to fuse a Kinect triangulation sensor and a small size Time-of-Flight camera to create a gaming sensor with superior resolution, acquisition range and accuracy. 1