Low-Cost Depth Camera Pose Tracking for Mobile Platforms

The KinectFusion algorithm is now used routinely to reconstruct dense 3D surfaces at real-time frame rates using a commodity depth camera. To achieve robust pose estimation, the method conducts the frame-to-model tracking during camera tracking that must inevitably accompany the memory-bound, GPU-as...

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Vydané v:2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct) s. 123 - 126
Hlavní autori: Insung Ihm, Youngwook Kim, Jaehyun Lee, Jiman Jeong, Ingu Park
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.09.2016
Predmet:
Cameras
Computational modeling
I.3.3 [Computer Graphics]: Picture/Image Generation-Digitizing and Scanning; I.4.8 [Image Processing and Computer Vision]: Scene Analysis-Tracking H.5.1 [Information Interfaces and Presentation]: Multimedia Information Systems-Artificial
Iterative closest point algorithm
Mobile communication
Pose estimation
Solid modeling
Three-dimensional displays
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Shrnutí:The KinectFusion algorithm is now used routinely to reconstruct dense 3D surfaces at real-time frame rates using a commodity depth camera. To achieve robust pose estimation, the method conducts the frame-to-model tracking during camera tracking that must inevitably accompany the memory-bound, GPU-assisted volumetric computations for the model manipulation, to which mobile processors are often more vulnerable than PC-based processors. In this paper, we present an effective camera-tracking method that is based on the computationally lighter frame-to-frame tracking method. This method's tendency toward rapid accumulation of pose estimation errors is suppressed effectively via a predictor-corrector technique. By removing the costly volumetric computations from the pose estimation process, our camera tracking system becomes more efficient in terms of both time and space complexity, offering a compact implementation of depth sensor-based camera tracking on low-end platforms such as mobile devices in addition to high-end PCs.
DOI:10.1109/ISMAR-Adjunct.2016.0057