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Novel view synthesis with light-weight view-dependent texture mapping for a stereoscopic HMD

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dc.contributor.author Rongsirigul, Thiwat en
dc.contributor.author Nakashima, Yuta en
dc.contributor.author Sato, Tomokazu en
dc.contributor.author Yokoya, Naokazu en
dc.date.accessioned 2018-03-28T05:33:57Z en
dc.date.available 2018-03-28T05:33:57Z en
dc.date.issued 2017-08-31 en
dc.identifier.issn 1945-788X en
dc.identifier.uri http://hdl.handle.net/10061/12227 en
dc.description ICME 2017: 18th IEEE International Conference on Multimedia and Expo, July 10-14, 2017, Hong Kong, China en
dc.description.abstract The proliferation of off-the-shelf head-mounted displays (HMDs) let end-users enjoy virtual reality applications, some of which render a real-world scene using a novel view synthesis (NVS) technique. View-dependent texture mapping (VDTM) has been studied for NVS due to its photo-realistic quality. The VDTM technique renders a novel view by adaptively selecting textures from the most appropriate images. However, this process is computationally expensive because VDTM scans every captured image. For stereoscopic HMDs, the situation is much worse because we need to render novel views once for each eye, almost doubling the cost. This paper proposes light-weight VDTM tailored for an HMD. In order to reduce the computational cost in VDTM, our method leverages the overlapping fields of view between a stereoscopic pair of HMD images and pruning the images to be scanned. We show that the proposed method drastically accelerates the VDTM process without spoiling the image quality through a user study. en
dc.language.iso en en
dc.publisher IEEE en
dc.rights Copyright c 2017, IEEE en
dc.subject Novel view synthesis en
dc.subject head-mounted displays en
dc.subject image-based rendering en
dc.title Novel view synthesis with light-weight view-dependent texture mapping for a stereoscopic HMD en
dc.type.nii Conference Paper en
dc.textversion author en
dc.identifier.spage 703 en
dc.identifier.epage 708 en
dc.relation.doi 10.1109/ICME.2017.8019417 en
dc.identifier.NAIST-ID 73298663 en
dc.identifier.NAIST-ID 73292633 en
dc.identifier.NAIST-ID 73292286 en


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