The Experts below are selected from a list of 831 Experts worldwide ranked by ideXlab platform
Christian Theobalt - One of the best experts on this subject based on the ideXlab platform.
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algorithms for 3d shape scanning with a depth camera
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Yan Cui, Sebastian Schuon, Sebastian Thrun, Didier Stricker, Christian TheobaltAbstract:We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a Time-of-Flight (ToF) camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology, they bear potential for economical production in big volumes. Our easy-to-use, cost-effective scanning solution, which is based on such a sensor, could make 3D scanning technology more accessible to everyday users. The algorithmic challenge we face is that the sensor's level of random noise is substantial and there is a nontrivial systematic bias. In this paper, we show the surprising result that 3D scans of reasonable Quality can also be obtained with a sensor of such Low Data Quality. Established filtering and scan alignment techniques from the literature fail to achieve this goal. In contrast, our algorithm is based on a new combination of a 3D superresolution method with a probabilistic scan alignment approach that explicitly takes into account the sensor's noise characteristics.
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CVPR - 3D shape scanning with a time-of-flight camera
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010Co-Authors: Sebastian Schuon, Sebastian Thrun, Derek Chan, Christian TheobaltAbstract:We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a time-of-flight camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology they bear potential for Low cost production in big volumes. Our easy-to-use, cost-effective scanning solution based on such a sensor could make 3D scanning technology more accessible to everyday users. The algorithmic challenge we face is that the sensor's level of random noise is substantial and there is a non-trivial systematic bias. In this paper we show the surprising result that 3D scans of reasonable Quality can also be obtained with a sensor of such Low Data Quality. Established filtering and scan alignment techniques from the literature fail to achieve this goal. In contrast, our algorithm is based on a new combination of a 3D superresolution method with a probabilistic scan alignment approach that explicitly takes into account the sensor's noise characteristics.
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3D shape scanning with a time-of-flight camera
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010Co-Authors: Yan Cui, Sebastian Schuon, Sebastian Thrun, Derek Chan, Christian TheobaltAbstract:We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a time-of-flight camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology they bear potential for Low cost production in big volumes. Our easy-to-use, cost-effective scanning solution based on such a sensor could make 3D scanning technology more accessible to everyday users. The algorithmic challenge we face is that the sensor's level of random noise is substantial and there is a non-trivial systematic bias. In this paper we show the surprising result that 3D scans of reasonable Quality can also be obtained with a sensor of such Low Data Quality. Established filtering and scan alignment techniques from the literature fail to achieve this goal. In contrast, our algorithm is based on a new combination of a 3D superresolution method with a probabilistic scan alignment approach that explicitly takes into account the sensor's noise characteristics.
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CVPR - LidarBoost: Depth superresolution for ToF 3D shape scanning
2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009Co-Authors: Sebastian Schuon, Christian Theobalt, James Davis, Sebastian ThrunAbstract:Depth maps captured with time-of-flight cameras have very Low Data Quality: the image resolution is rather limited and the level of random noise contained in the depth maps is very high. Therefore, such flash lidars cannot be used out of the box for high-Quality 3D object scanning. To solve this problem, we present LidarBoost, a 3D depth superresolution method that combines several Low resolution noisy depth images of a static scene from slightly displaced viewpoints, and merges them into a high-resolution depth image. We have developed an optimization framework that uses a Data fidelity term and a geometry prior term that is tailored to the specific characteristics of flash lidars. We demonstrate both visually and quantitatively that LidarBoost produces better results than previous methods from the literature.
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lidarboost depth superresolution for tof 3d shape scanning
Computer Vision and Pattern Recognition, 2009Co-Authors: Sebastian Schuon, Christian Theobalt, James Davis, Sebastian ThrunAbstract:Depth maps captured with time-of-flight cameras have very Low Data Quality: the image resolution is rather limited and the level of random noise contained in the depth maps is very high. Therefore, such flash lidars cannot be used out of the box for high-Quality 3D object scanning. To solve this problem, we present LidarBoost, a 3D depth superresolution method that combines several Low resolution noisy depth images of a static scene from slightly displaced viewpoints, and merges them into a high-resolution depth image. We have developed an optimization framework that uses a Data fidelity term and a geometry prior term that is tailored to the specific characteristics of flash lidars. We demonstrate both visually and quantitatively that LidarBoost produces better results than previous methods from the literature.
Sebastian Thrun - One of the best experts on this subject based on the ideXlab platform.
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algorithms for 3d shape scanning with a depth camera
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Yan Cui, Sebastian Schuon, Sebastian Thrun, Didier Stricker, Christian TheobaltAbstract:We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a Time-of-Flight (ToF) camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology, they bear potential for economical production in big volumes. Our easy-to-use, cost-effective scanning solution, which is based on such a sensor, could make 3D scanning technology more accessible to everyday users. The algorithmic challenge we face is that the sensor's level of random noise is substantial and there is a nontrivial systematic bias. In this paper, we show the surprising result that 3D scans of reasonable Quality can also be obtained with a sensor of such Low Data Quality. Established filtering and scan alignment techniques from the literature fail to achieve this goal. In contrast, our algorithm is based on a new combination of a 3D superresolution method with a probabilistic scan alignment approach that explicitly takes into account the sensor's noise characteristics.
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CVPR - 3D shape scanning with a time-of-flight camera
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010Co-Authors: Sebastian Schuon, Sebastian Thrun, Derek Chan, Christian TheobaltAbstract:We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a time-of-flight camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology they bear potential for Low cost production in big volumes. Our easy-to-use, cost-effective scanning solution based on such a sensor could make 3D scanning technology more accessible to everyday users. The algorithmic challenge we face is that the sensor's level of random noise is substantial and there is a non-trivial systematic bias. In this paper we show the surprising result that 3D scans of reasonable Quality can also be obtained with a sensor of such Low Data Quality. Established filtering and scan alignment techniques from the literature fail to achieve this goal. In contrast, our algorithm is based on a new combination of a 3D superresolution method with a probabilistic scan alignment approach that explicitly takes into account the sensor's noise characteristics.
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3D shape scanning with a time-of-flight camera
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010Co-Authors: Yan Cui, Sebastian Schuon, Sebastian Thrun, Derek Chan, Christian TheobaltAbstract:We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a time-of-flight camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology they bear potential for Low cost production in big volumes. Our easy-to-use, cost-effective scanning solution based on such a sensor could make 3D scanning technology more accessible to everyday users. The algorithmic challenge we face is that the sensor's level of random noise is substantial and there is a non-trivial systematic bias. In this paper we show the surprising result that 3D scans of reasonable Quality can also be obtained with a sensor of such Low Data Quality. Established filtering and scan alignment techniques from the literature fail to achieve this goal. In contrast, our algorithm is based on a new combination of a 3D superresolution method with a probabilistic scan alignment approach that explicitly takes into account the sensor's noise characteristics.
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CVPR - LidarBoost: Depth superresolution for ToF 3D shape scanning
2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009Co-Authors: Sebastian Schuon, Christian Theobalt, James Davis, Sebastian ThrunAbstract:Depth maps captured with time-of-flight cameras have very Low Data Quality: the image resolution is rather limited and the level of random noise contained in the depth maps is very high. Therefore, such flash lidars cannot be used out of the box for high-Quality 3D object scanning. To solve this problem, we present LidarBoost, a 3D depth superresolution method that combines several Low resolution noisy depth images of a static scene from slightly displaced viewpoints, and merges them into a high-resolution depth image. We have developed an optimization framework that uses a Data fidelity term and a geometry prior term that is tailored to the specific characteristics of flash lidars. We demonstrate both visually and quantitatively that LidarBoost produces better results than previous methods from the literature.
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lidarboost depth superresolution for tof 3d shape scanning
Computer Vision and Pattern Recognition, 2009Co-Authors: Sebastian Schuon, Christian Theobalt, James Davis, Sebastian ThrunAbstract:Depth maps captured with time-of-flight cameras have very Low Data Quality: the image resolution is rather limited and the level of random noise contained in the depth maps is very high. Therefore, such flash lidars cannot be used out of the box for high-Quality 3D object scanning. To solve this problem, we present LidarBoost, a 3D depth superresolution method that combines several Low resolution noisy depth images of a static scene from slightly displaced viewpoints, and merges them into a high-resolution depth image. We have developed an optimization framework that uses a Data fidelity term and a geometry prior term that is tailored to the specific characteristics of flash lidars. We demonstrate both visually and quantitatively that LidarBoost produces better results than previous methods from the literature.
Sebastian Schuon - One of the best experts on this subject based on the ideXlab platform.
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algorithms for 3d shape scanning with a depth camera
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Yan Cui, Sebastian Schuon, Sebastian Thrun, Didier Stricker, Christian TheobaltAbstract:We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a Time-of-Flight (ToF) camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology, they bear potential for economical production in big volumes. Our easy-to-use, cost-effective scanning solution, which is based on such a sensor, could make 3D scanning technology more accessible to everyday users. The algorithmic challenge we face is that the sensor's level of random noise is substantial and there is a nontrivial systematic bias. In this paper, we show the surprising result that 3D scans of reasonable Quality can also be obtained with a sensor of such Low Data Quality. Established filtering and scan alignment techniques from the literature fail to achieve this goal. In contrast, our algorithm is based on a new combination of a 3D superresolution method with a probabilistic scan alignment approach that explicitly takes into account the sensor's noise characteristics.
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CVPR - 3D shape scanning with a time-of-flight camera
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010Co-Authors: Sebastian Schuon, Sebastian Thrun, Derek Chan, Christian TheobaltAbstract:We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a time-of-flight camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology they bear potential for Low cost production in big volumes. Our easy-to-use, cost-effective scanning solution based on such a sensor could make 3D scanning technology more accessible to everyday users. The algorithmic challenge we face is that the sensor's level of random noise is substantial and there is a non-trivial systematic bias. In this paper we show the surprising result that 3D scans of reasonable Quality can also be obtained with a sensor of such Low Data Quality. Established filtering and scan alignment techniques from the literature fail to achieve this goal. In contrast, our algorithm is based on a new combination of a 3D superresolution method with a probabilistic scan alignment approach that explicitly takes into account the sensor's noise characteristics.
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3D shape scanning with a time-of-flight camera
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010Co-Authors: Yan Cui, Sebastian Schuon, Sebastian Thrun, Derek Chan, Christian TheobaltAbstract:We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a time-of-flight camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology they bear potential for Low cost production in big volumes. Our easy-to-use, cost-effective scanning solution based on such a sensor could make 3D scanning technology more accessible to everyday users. The algorithmic challenge we face is that the sensor's level of random noise is substantial and there is a non-trivial systematic bias. In this paper we show the surprising result that 3D scans of reasonable Quality can also be obtained with a sensor of such Low Data Quality. Established filtering and scan alignment techniques from the literature fail to achieve this goal. In contrast, our algorithm is based on a new combination of a 3D superresolution method with a probabilistic scan alignment approach that explicitly takes into account the sensor's noise characteristics.
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CVPR - LidarBoost: Depth superresolution for ToF 3D shape scanning
2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009Co-Authors: Sebastian Schuon, Christian Theobalt, James Davis, Sebastian ThrunAbstract:Depth maps captured with time-of-flight cameras have very Low Data Quality: the image resolution is rather limited and the level of random noise contained in the depth maps is very high. Therefore, such flash lidars cannot be used out of the box for high-Quality 3D object scanning. To solve this problem, we present LidarBoost, a 3D depth superresolution method that combines several Low resolution noisy depth images of a static scene from slightly displaced viewpoints, and merges them into a high-resolution depth image. We have developed an optimization framework that uses a Data fidelity term and a geometry prior term that is tailored to the specific characteristics of flash lidars. We demonstrate both visually and quantitatively that LidarBoost produces better results than previous methods from the literature.
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lidarboost depth superresolution for tof 3d shape scanning
Computer Vision and Pattern Recognition, 2009Co-Authors: Sebastian Schuon, Christian Theobalt, James Davis, Sebastian ThrunAbstract:Depth maps captured with time-of-flight cameras have very Low Data Quality: the image resolution is rather limited and the level of random noise contained in the depth maps is very high. Therefore, such flash lidars cannot be used out of the box for high-Quality 3D object scanning. To solve this problem, we present LidarBoost, a 3D depth superresolution method that combines several Low resolution noisy depth images of a static scene from slightly displaced viewpoints, and merges them into a high-resolution depth image. We have developed an optimization framework that uses a Data fidelity term and a geometry prior term that is tailored to the specific characteristics of flash lidars. We demonstrate both visually and quantitatively that LidarBoost produces better results than previous methods from the literature.
Yan Cui - One of the best experts on this subject based on the ideXlab platform.
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algorithms for 3d shape scanning with a depth camera
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Yan Cui, Sebastian Schuon, Sebastian Thrun, Didier Stricker, Christian TheobaltAbstract:We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a Time-of-Flight (ToF) camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology, they bear potential for economical production in big volumes. Our easy-to-use, cost-effective scanning solution, which is based on such a sensor, could make 3D scanning technology more accessible to everyday users. The algorithmic challenge we face is that the sensor's level of random noise is substantial and there is a nontrivial systematic bias. In this paper, we show the surprising result that 3D scans of reasonable Quality can also be obtained with a sensor of such Low Data Quality. Established filtering and scan alignment techniques from the literature fail to achieve this goal. In contrast, our algorithm is based on a new combination of a 3D superresolution method with a probabilistic scan alignment approach that explicitly takes into account the sensor's noise characteristics.
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3D shape scanning with a time-of-flight camera
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010Co-Authors: Yan Cui, Sebastian Schuon, Sebastian Thrun, Derek Chan, Christian TheobaltAbstract:We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a time-of-flight camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology they bear potential for Low cost production in big volumes. Our easy-to-use, cost-effective scanning solution based on such a sensor could make 3D scanning technology more accessible to everyday users. The algorithmic challenge we face is that the sensor's level of random noise is substantial and there is a non-trivial systematic bias. In this paper we show the surprising result that 3D scans of reasonable Quality can also be obtained with a sensor of such Low Data Quality. Established filtering and scan alignment techniques from the literature fail to achieve this goal. In contrast, our algorithm is based on a new combination of a 3D superresolution method with a probabilistic scan alignment approach that explicitly takes into account the sensor's noise characteristics.
Christian S Jensen - One of the best experts on this subject based on the ideXlab platform.
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Deep Representation Learning for Trajectory Similarity Computation
2018 IEEE 34th International Conference on Data Engineering (ICDE), 2018Co-Authors: Xiucheng Li, Kaiqi Zhao, Gao Cong, Christian S JensenAbstract:Trajectory similarity computation is fundamental functionality with many applications such as animal migration pattern studies and vehicle trajectory mining to identify popular routes and similar drivers. While a trajectory is a continuous curve in some spatial domain, e.g., 2D Euclidean space, trajectories are often represented by point sequences. Existing approaches that compute similarity based on point matching suffer from the problem that they treat two different point sequences differently even when the sequences represent the same trajectory. This is particularly a problem when the point sequences are non-uniform, have Low sampling rates, and have noisy points. We propose the first deep learning approach to learning representations of trajectories that is robust to Low Data Quality, thus supporting accurate and efficient trajectory similarity computation and search. Experiments show that our method is capable of higher accuracy and is at least one order of magnitude faster than the state-of-the-art methods for k-nearest trajectory search.
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ICDE - Deep Representation Learning for Trajectory Similarity Computation
2018 IEEE 34th International Conference on Data Engineering (ICDE), 2018Co-Authors: Xiucheng Li, Kaiqi Zhao, Gao Cong, Christian S JensenAbstract:Trajectory similarity computation is fundamental functionality with many applications such as animal migration pattern studies and vehicle trajectory mining to identify popular routes and similar drivers. While a trajectory is a continuous curve in some spatial domain, e.g., 2D Euclidean space, trajectories are often represented by point sequences. Existing approaches that compute similarity based on point matching suffer from the problem that they treat two different point sequences differently even when the sequences represent the same trajectory. This is particularly a problem when the point sequences are non-uniform, have Low sampling rates, and have noisy points. We propose the first deep learning approach to learning representations of trajectories that is robust to Low Data Quality, thus supporting accurate and efficient trajectory similarity computation and search. Experiments show that our method is capable of higher accuracy and is at least one order of magnitude faster than the state-of-the-art methods for k-nearest trajectory search.
