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ISMAR 2014 - Sep 10-12 - Munich, Germany

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ISMAR Papers for Session "Tracking"

Session : 
Date & Time : September 11 04:00 pm - 06:00 pm
Location : HS1
Chair : Georg Klein, Microsoft Corporation
Papers : 
Pixel-Wise Closed-Loop Registration in Video-Based Augmented Reality
Authors: Feng Zheng, Dieter Schmalstieg, Gregory Welch
Abstract :
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.
Semi-Dense Visual Odometry for AR on a Smartphone
Authors: Thomas Schöps, Jakob Engel, Daniel Cremers
Abstract :
We present a direct monocular visual odometry system which runs in real-time on a smartphone. Being a direct method, it tracks and maps on the images themselves instead of extracted features such as keypoints. New images are tracked using direct image alignment, while geometry is represented in the form of a semi-dense depth map. Depth is estimated by filtering over many small-baseline, pixel-wise stereo comparisons. This leads to significantly less outliers and allows to map and use all image regions with sufficient gradient, including edges. We show how a simple world model for AR applications can be derived from semi-dense depth maps, and demonstrate the practical applicability in the context of an AR application in which simulated objects can collide with real geometry.
Sticky Projections - A New Approach to Interactive Shader Lamp Tracking
Authors: Christoph Resch, Peter Keitler, Gudrun Klinker
Abstract :
Shader lamps can augment physical objects with projected virtual replications using a camera-projector system, provided that the physical and virtual object are well registered. Precise registration and tracking has been a cumbersome and intrusive process in the past. In this paper, we present a new method for tracking arbitrarily shaped physical objects interactively. In contrast to previous approaches our system is mobile and makes solely use of the projection of the virtual replication to track the physical object and "stick" the projection to it. Our method consists of two stages, a fast pose initialization based on structured light patterns and a non-intrusive frame-by-frame tracking based on features detected in the projection. In the initialization phase a dense point cloud of the physical object is reconstructed and precisely matched to the virtual model to perfectly overlay the projection. During the tracking phase, a radiometrically corrected virtual camera view based on the current pose prediction is rendered and compared to the captured image. Matched features are triangulated providing a sparse set of surface points that is robustly aligned to the virtual model. The alignment transformation serves as an input for the new pose prediction. Quantitative experiments show that our approach can robustly track complex objects at interactive rates.
Dense Planar SLAM
Authors: Renato Salas-Moreno, Ben Glocker, Paul Kelly, Andrew Davison
Abstract :
Using higher-level entities during mapping has the potential to improve camera localisation performance and give substantial perception capabilities to real-time 3D SLAM systems. We present an efficient new real-time approach which densely maps an environment using bounded planes and surfels extracted from depth images (like those produced by RGB-D sensors or dense multi-view stereo reconstruction). Our method offers the every-pixel descriptive power of the latest dense SLAM approaches, but takes advantage directly of the planarity of many parts of real-world scenes via a data-driven process to directly regularize planar regions and represent their accurate extent efficiently using an occupancy approach with on-line compression. Large areas can be mapped efficiently and with useful semantic planar structure which enables intuitive and useful AR applications such as using any wall or other planar surface in a scene to display a user's content.
Real-time Deformation, Registration and Tracking of Solids Based on Physical Simulation
Authors: Ibai Leizea, Hugo Álvarez, Iker Aguinaga, Diego Borro
Abstract :
This paper proposes a novel approach to registering deformations of 3D non-rigid objects for Augmented Reality applications. Our prototype is able to handle different types of objects in real-time regardless of their geometry and appearance (with and without texture) with the support of an RGB-D camera. During an automatic offline stage, the model is processed in order to extract the data that serves as input for a physics-based simulation. Using its output, the deformations of the model are estimated by considering the simulated behaviour as a constraint. Furthermore, our framework incorporates a tracking method based on templates in order to detect the object in the scene and continuously update the camera pose without any user intervention. Therefore, it is a complete solution that extends from tracking to deformation formulation for either textured or untextured objects regardless of their geometrical shape. Our proposal focuses on providing a correct visual with a low computational cost. Experiments with real and synthetic data demonstrate the visual accuracy and the performance of our approach.

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