This paper presents a scalable model-based approach for 3d scene reconstruction using a moving RGB-d camera. The proposed approach enhances the accuracy of pose estimation due to exploiting the rich information in the...
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ISBN:
(纸本)9783319518114;9783319518107
This paper presents a scalable model-based approach for 3d scene reconstruction using a moving RGB-d camera. The proposed approach enhances the accuracy of pose estimation due to exploiting the rich information in the multi-channel RGB-dimagedata. Our approach has lots of advantages on the reconstruction quality of the 3d scene as compared with the conventional approaches using sparse features for pose estimation. The pre-learnedimage-based 3d model provides multiple templates for sampled views of the model, which are used to estimate the poses of the frames in the input RGB-d video without the need of a priori internal and external camera parameters. Through template-to-frame registration, the reconstructed3d scene can be loaded in an augmented reality (AR) environment to facilitate displaying, interaction, and rendering of an image-based AR application. Finally, we verify the ability of the established reconstruction system on publicly available benchmark datasets, and compare it with the sate-of-the-art pose estimation algorithms. The results indicate that our approach outperforms the compared methods on the accuracy of pose estimation.
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