Model-Based 3D Scene Reconstruction Using a Moving RGB-D Camera

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-D image data. 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-learned image-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 reconstructed 3D 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.