Abstract
Acquiring accurate dense depth maps with low computational complexity is crucial for real-time applications that require 3D reconstruction. The current sensors capable of generating dense maps are expensive and bulky, while compact low-cost sensors can only generate the sparse map measurements reliably. To overcome this predicament, we propose an efficient stereo analysis algorithm that constructs a dense disparity map from the sparse measurements. Our approach generates a dense disparity map with low computational complexity using local methods. The algorithm has much less computation time than the existing dense stereo matching techniques and has a high visual accuracy. Experiments results performed on KITTI and Middlebury datasets show that our algorithm has much less running time while providing accurate disparity maps.
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Bhandari, P., Wu, M., Aslam, N., Lam, SK., Kolekar, M. (2020). Efficient Sparse to Dense Stereo Matching Technique. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1022. Springer, Singapore. https://doi.org/10.1007/978-981-32-9088-4_14
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DOI: https://doi.org/10.1007/978-981-32-9088-4_14
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