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Weighted Motion Averaging for the Registration of Multi-View Range Scans
Multi-view registration is a fundamental but challenging problem in 3D r...
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Plane-Based Optimization of Geometry and Texture for RGB-D Reconstruction of Indoor Scenes
We present a novel approach to reconstruct RGB-D indoor scene with plane...
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Reducing Drift in Structure from Motion using Extended Features
Low-frequency long-range errors (drift) are an endemic problem in 3D str...
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Planar Geometry and Latest Scene Recovery from a Single Motion Blurred Image
Existing works on motion deblurring either ignore the effects of depth-d...
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A Novel Method for Extrinsic Calibration of Multiple RGB-D Cameras Using Descriptor-Based Patterns
This letter presents a novel method to estimate the relative poses betwe...
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Fine-To-Coarse Global Registration of RGB-D Scans
RGB-D scanning of indoor environments is important for many applications...
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Optimal Reconstruction with a Small Number of Views
Estimating positions of world points from features observed in images is...
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Plane Pair Matching for Efficient 3D View Registration
We present a novel method to estimate the motion matrix between overlapping pairs of 3D views in the context of indoor scenes. We use the Manhattan world assumption to introduce lightweight geometric constraints under the form of planes into the problem, which reduces complexity by taking into account the structure of the scene. In particular, we define a stochastic framework to categorize planes as vertical or horizontal and parallel or non-parallel. We leverage this classification to match pairs of planes in overlapping views with point-of-view agnostic structural metrics. We propose to split the motion computation using the classification and estimate separately the rotation and translation of the sensor, using a quadric minimizer. We validate our approach on a toy example and present quantitative experiments on a public RGB-D dataset, comparing against recent state-of-the-art methods. Our evaluation shows that planar constraints only add low computational overhead while improving results in precision when applied after a prior coarse estimate. We conclude by giving hints towards extensions and improvements of current results.
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