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ISMAR 2014 - Sep 10-12 - Munich, Germany

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Pixel-Wise Closed-Loop Registration in Video-Based Augmented Reality


Event TitleSession TitleChairRoomStartEnd
TrackingTrackingGeorg Klein, Microsoft CorporationHS111 Sep, 2014 04:00 PM11 Sep, 2014 06:00 PM
Feng Zheng
Dieter Schmalstieg
Gregory Welch
In Augmented Reality (AR), visible misregistration can be caused by many inherent error sources, such as errors in tracking, calibration, and modeling. In this paper we present a novel pixel-wise closed-loop registration framework that can automatically detect and correct registration errors using a reference model comprised of the real scene model and the desired virtual augmentations. Registration errors are corrected in both global world space via camera pose refinement, and local screen space via pixel-wise corrections, resulting in spatially accurate and visually coherent registration. Specifically we present a registration-enforcing model-based tracking approach that weights important image regions while refining the camera pose estimates (from any conventional tracking method) to achieve better registration, even in the case of modeling errors. To deal with remaining errors, which can be rigid or non-rigid, we compute the optical flow between the camera image and the real model image rendered with the refined pose, enabling direct screen-space pixel-wise corrections to misregistration. The estimated flow field can be applied to improve registration in two distinct ways: (1) forward warping of modeled on-real-object-surface augmentations (e.g., object re-texturing) into the camera image, leading to surface details that are not present in the virtual object; and (2) backward warping of the camera image into the real scene model, preserving the full use of the dense geometry buffer (depth in particular) provided by the combined real-virtual model for registration, leading to pixel accurate real-virtual occlusion. We discuss the trade-offs between, and different use cases of, forward and backward warping with model-based tracking in terms of specific properties for registration. We demonstrate the efficacy of our approach with both simulated and real data.

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