Depth sensor noise
First Claim
1. A computer-implemented method for determining a function configured for adding a noise of a type of depth sensor, the type of depth sensor being a category of depth sensors of a similar or an at least substantially same constitution such that depth sensors present a similar or an at least substantially same noise, to an input depth map, the method comprising:
- forming a learning dataset, the learning dataset including a plurality of noiseless depth maps each associated to a respective noisy depth map, each noiseless depth map and respective noisy depth map corresponding to a same one of a plurality of depth sensor positionings in one or more scenes, the forming including;
acquiring each noisy depth map by performing depth measurements with a physical instance of the type of depth sensor, the physical instance of the type of depth sensor being performed with a depth sensor of the type of depth sensor, andvirtually calculating each noiseless depth map by determining each noiseless depth map fully based on numerical data and without any physical acquisition; and
learning the function based on the learning dataset, the learning of the function including adjusting parameters of the function by performing machine-learning on the learning dataset such that applying the learnt function to a given noiseless depth map of the learning dataset leads to a result corresponding to the respective noisy depth map associated with the given noiseless depth map in the dataset.
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Accused Products
Abstract
Described is a computer-implemented method for learning a function configured for reconstructing, for a class of real objects, a 3D modeled object that represents an instance of the class from a depth map of the instance. The method comprises providing a parametric model of the class, creating a database, and learning the function with the database. The method improves the 3D reconstruction of a real object from a depth map.
13 Citations
24 Claims
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1. A computer-implemented method for determining a function configured for adding a noise of a type of depth sensor, the type of depth sensor being a category of depth sensors of a similar or an at least substantially same constitution such that depth sensors present a similar or an at least substantially same noise, to an input depth map, the method comprising:
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forming a learning dataset, the learning dataset including a plurality of noiseless depth maps each associated to a respective noisy depth map, each noiseless depth map and respective noisy depth map corresponding to a same one of a plurality of depth sensor positionings in one or more scenes, the forming including; acquiring each noisy depth map by performing depth measurements with a physical instance of the type of depth sensor, the physical instance of the type of depth sensor being performed with a depth sensor of the type of depth sensor, and virtually calculating each noiseless depth map by determining each noiseless depth map fully based on numerical data and without any physical acquisition; and learning the function based on the learning dataset, the learning of the function including adjusting parameters of the function by performing machine-learning on the learning dataset such that applying the learnt function to a given noiseless depth map of the learning dataset leads to a result corresponding to the respective noisy depth map associated with the given noiseless depth map in the dataset. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A non-transitory computer readable medium having recorded thereon a function learnable according to a computer-implemented method for determining a function configured for adding a noise of a type of depth sensor, the type of depth sensor being a category of depth sensors of a similar or an at least substantially same constitution such that depth sensors present a similar or an at least substantially same noise, to an input depth map, the method comprising:
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forming a learning dataset, the learning dataset including a plurality of noiseless depth maps each associated to a respective noisy depth map, each noiseless depth map and respective noisy depth map corresponding to a same one of a plurality of depth sensor positionings in one or more scenes, the forming including; acquiring each noisy depth map by performing depth measurements with a physical instance of the type of depth sensor, the physical instance of the type of depth sensor being performed with a depth sensor of the type of depth sensor, and virtually calculating each noiseless depth map by determining each noiseless depth map fully based on numerical data and without any physical acquisition; and learning the function based on the learning dataset, the learning of the function including adjusting parameters of the function by performing machine-learning on the learning dataset such that applying the learnt function to a given noiseless depth map of the learning dataset leads to a result corresponding to the respective noisy depth map associated with the given noiseless depth map in the dataset.
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15. A computer-implemented method for generating a depth map based on a depth sensor positioning in a scene and on a predetermined model of the scene, the method comprising:
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virtually calculating a noiseless depth map based on the depth sensor positioning and on the predetermined model of the scene; and applying, to the calculated noiseless depth map, a function learnable according to a further computer-implemented method for determining a function configured for adding a noise of a type of depth sensor, the type of depth sensor being a category of depth sensors of a similar or an at least substantially same constitution such that depth sensors present a similar or an at least substantially same noise, to an input depth map, including; forming a learning dataset, the learning dataset including a plurality of noiseless depth maps each associated to a respective noisy depth map, each noiseless depth map and respective noisy depth map corresponding to a same one of a plurality of depth sensor positionings in one or more scenes, the forming including; acquiring each noisy depth map by performing depth measurements with a physical instance of the type of depth sensor, the physical instance of the type of depth sensor being performed with a depth sensor of the type of depth sensor, and virtually calculating each noiseless depth map by determining each noiseless depth map fully based on numerical data and without any physical acquisition; and learning the function based on the learning dataset, the learning of the function including adjusting parameters of the function by performing machine-learning on the learning dataset such that applying the learnt function to a given noiseless depth map of the learning dataset leads to a result corresponding to the respective noisy depth map associated with the given noiseless depth map in the dataset.
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16. A non-transitory computer readable medium having recorded thereon a computer program comprising instructions for performing a computer-implemented method for determining a function configured for adding a noise of a type of depth sensor, the type of depth sensor being a category of depth sensors of a similar or an at least substantially same constitution such that depth sensors present a similar or an at least substantially same noise, to an input depth map, the method comprising:
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forming a learning dataset, the learning dataset including a plurality of noiseless depth maps each associated to a respective noisy depth map, each noiseless depth map and respective noisy depth map corresponding to a same one of a plurality of depth sensor positionings in one or more scenes, the forming including; acquiring each noisy depth map by performing depth measurements with a physical instance of the type of depth sensor, the physical instance of the type of depth sensor being performed with a depth sensor of the type of depth sensor, and virtually calculating each noiseless depth map by determining each noiseless depth map fully based on numerical data and without any physical acquisition; and learning the function based on the learning dataset, the learning of the function including adjusting parameters of the function by performing machine-learning on the learning dataset such that applying the learnt function to a given noiseless depth map of the learning dataset leads to a result corresponding to the respective noisy depth map associated with the given noiseless depth map in the dataset.
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17. A computer system comprising:
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a processor coupled to the memory, the memory having recorded thereon a computer program comprising instructions for performing a computer-implemented method for determining a function configured for adding a noise of a type of depth sensor, the type of depth sensor being a category of depth sensors of a similar or an at least substantially same constitution such that depth sensors present a similar or an at least substantially same noise, to an input depth map, the instructions when executed by the processor cause the process to be configured to; form a learning dataset, the learning dataset including a plurality of noiseless depth maps each associated to a respective noisy depth map, each noiseless depth map and respective noisy depth map corresponding to a same one of a plurality of depth sensor positionings in one or more scenes, the processor being configured to form the learning dataset by being further configured to acquire each noisy depth map by performing depth measurements with a physical instance of the type of depth sensor, the physical instance of the type of depth sensor being performed with a depth sensor of the type of depth sensor, and virtually calculate each noiseless depth map by determining each noiseless depth map fully based on numerical data and without any physical acquisition; and learn the function based on the learning dataset, the learning of the function including adjusting parameters of the function by performing machine-learning on the learning dataset such that applying the learnt function to a given noiseless depth map of the learning dataset leads to a result corresponding to the respective noisy depth map associated with the given noiseless depth map in the dataset. - View Dependent Claims (18, 19, 20, 21, 22, 23, 24)
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Specification