Abstract
A powerful approach for managing scene complexity is to sample the scene with a set of images. However, conventional images from nearby viewpoints have a high level of redundancy, which reduces scene sampling efficiency. We present non-redundant rendering, which detects and avoids redundant samples as the image is computed. We show that non-redundant rendering leads to improved scene sampling quality according to several view-independent and view-dependent metrics, compared to conventional scene discretization using redundant images and compared to depth peeling. Non-redundant images have a higher degree of fragmentation and, therefore, conventional approaches for scene reconstruction from samples are ineffective. We present a novel reconstruction approach that is well suited to scene discretization by non-redundant rendering. Finally, we apply non-redundant rendering and scene reconstruction techniques to soft shadow rendering where we show that our approach has an accuracy advantage over conventional images and over depth peeling.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China through Projects 61272349, 61190121 and 61190125, by the National High Technology Research and Development Program of China through 863 Program No. 2013AA01A604. Naiwen Xie gratefully acknowledges financial support from China Scholarship Council (CSC) through No. 201506020037.
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Xie, N., Wang, L. & Popescu, V. Non-redundant rendering for efficient multi-view scene discretization. Vis Comput 33, 1555–1569 (2017). https://doi.org/10.1007/s00371-016-1300-6
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DOI: https://doi.org/10.1007/s00371-016-1300-6