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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| Vydáno v: | 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct) s. 123 - 126 |
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IEEE
01.09.2016
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Ingu Park Jiman Jeong Jaehyun Lee Youngwook Kim Insung Ihm |
| Author_xml | – sequence: 1 surname: Insung Ihm fullname: Insung Ihm email: ihm@sogang.ac.kr organization: Dept. of Comput. Sci. & Eng., Sogang Univ., Seoul, South Korea – sequence: 2 surname: Youngwook Kim fullname: Youngwook Kim email: kimyu7@sogang.ac.kr organization: Dept. of Comput. Sci. & Eng., Sogang Univ., Seoul, South Korea – sequence: 3 surname: Jaehyun Lee fullname: Jaehyun Lee email: kidsnow@sogang.ac.kr organization: Dept. of Comput. Sci. & Eng., Sogang Univ., Seoul, South Korea – sequence: 4 surname: Jiman Jeong fullname: Jiman Jeong email: sixzone11@sogang.ac.kr organization: Dept. of Comput. Sci. & Eng., Sogang Univ., Seoul, South Korea – sequence: 5 surname: Ingu Park fullname: Ingu Park email: ssault@ncsoft.com organization: NCSOFT Corp., South Korea |
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| Snippet | 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... |
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| SubjectTerms | 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 |
| Title | Low-Cost Depth Camera Pose Tracking for Mobile Platforms |
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