An active depth sensing approach by laser speckle projection system is proposed. After capturing the speckle pattern with an infrared digital camera, we extract the pure speckle pattern using a direct-global separation method. Then the pure speckles are represented by Census binary features. By evaluating the matching cost and uniqueness between the real-time image and the reference image, robust correspondences are selected as support points. After that, we build a disparity grid and propose a generative graphical model to compute disparities. An iterative approach is designed to propagate the messages between blocks and update the model. Finally, a dense depth map can be obtained by subpixel interpolation and transformation. The experimental evaluations demonstrate the effectiveness and efficiency of our approach.
KEYWORDS: Magnetorheological finishing, Machine learning, Digital filtering, RGB color model, Active remote sensing, Image fusion, Optical filters, Optimization (mathematics), 3D image processing, 3D vision
In this paper, we consider the task of hole filling in depth maps, with the help of an associated color image. We take a supervised learning approach to solve this problem. The model is learnt from the training set, which contain pixels that have depth values. Then we apply supervised learning to predict the depth values in the holes. Our model uses a regional Markov Random Field (MRF) that incorporates multiscale absolute and relative features (computed from the color image), and models depths not only at individual points but also between adjacent points. The experiments show that the proposed approach is able to recover fairly accurate depth values and achieve a high quality depth map.
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