Mingsong Dou (2), Sameh Khamis (2), Yury Degtyarev (1), Philip Davidson (2), Sean Ryan Fanello (2), Adarsh Kowdle (2), Sergio Orts Escolano (2), Christoph Rhemann (2), David Kim (2), Jonathan Taylor (2), Pushmeet Kohli (1), Vladimir Tankovich (2), Shahram Izadi (2)
Note: This work was conducted at Microsoft Research
(1) Microsoft Research (2) perceptiveIO
We contribute a new pipeline for live multi-view performance capture, generating temporally coherent high-quality reconstructions in real-time. Our algorithm supports both incremental reconstruction, improving the surface estimation over time, as well as parameterizing the nonrigid scene motion. Our approach is highly robust to both large frame-to-frame motion and topology changes, allowing us to reconstruct extremely challenging scenes. We demonstrate advantages over related real-time techniques that either deform an online generated template or continually fuse depth data nonrigidly into a single reference model. Finally, we show geometric reconstruction results on par with offline methods which require orders of magnitude more processing time and many more RGBD cameras.
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