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A much more appealing approach is to use automulti-scopic displays that provide a glasses-free 3D experience to multiple viewers. A technical challenge is the lack of native multiview content that is required to deliver a proper view of the scene for every viewpoint. Our approach takes advantage of the abundance of stereoscopic 3D movies. We propose a real-time system that can convert stereoscopic video to a high-quality multiview video that can be directly fed to automultiscopic displays. Our algorithm uses a wavelet-based decomposition of stereoscopic images with per-wavelet disparity estimation. A key to our solution lies in combining Lagrangian and Eulerian approaches for both the disparity estimation and novel view synthesis, which leverages the complementary advantages of both techniques. The solution preserves all the features of Eulerian methods, e.g., subpixel accuracy, high performance, robustness to ambiguous depth cases, and easy integration of inter-view aliasing while maintaining the advantages of Lagrangian approaches, e.g., robustness to large disparities and possibility of performing non-trivial disparity manipulations through both view extrapolation and interpolation. The method achieves real-time performance on current GPUs. Its design also enables an easy hardware implementation that is demonstrated using a field-programmable gate array. We analyze the visual quality and robustness of our technique on a number of synthetic and real-world examples. We also perform a user experiment which demonstrates benefits of the technique when compared to existing solutions.<\/jats:p>","DOI":"10.1145\/3072959.3073617","type":"journal-article","created":{"date-parts":[[2017,7,21]],"date-time":"2017-07-21T12:24:07Z","timestamp":1500639847000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["3DTV at home"],"prefix":"10.1145","volume":"36","author":[{"given":"Petr","family":"Kellnhofer","sequence":"first","affiliation":[{"name":"MIT CSAIL and MPI Informatik"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piotr","family":"Didyk","sequence":"additional","affiliation":[{"name":"Saarland University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Szu-Po","family":"Wang","sequence":"additional","affiliation":[{"name":"MIT CSAIL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pitchaya","family":"Sitthi-Amorn","sequence":"additional","affiliation":[{"name":"Chulalongkorn University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Freeman","sequence":"additional","affiliation":[{"name":"MIT CSAIL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fredo","family":"Durand","sequence":"additional","affiliation":[{"name":"MIT CSAIL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wojciech","family":"Matusik","sequence":"additional","affiliation":[{"name":"MIT CSAIL"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,7,20]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2980179.2980257"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2003.1217603"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12291"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-79567-4_164"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366203"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2508363.2508376"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2661229.2661248"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487228.2487230"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2011.6012182"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00056772"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/1049-9660(91)90027-M"},{"key":"e_1_2_2_12_1","volume-title":"DeepStereo: Learning to predict new views from the world's imagery. arXiv preprint arXiv:1506.06825","author":"Flynn John","year":"2015","unstructured":"John Flynn , Ivan Neulander , James Philbin , and Noah Snavely . 2015. 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