Boundless Reconstruction Using Regularized 3D Fusion

Rajput, M.A.A.; Funk, E.; Borner, A.; Hellwich, O.; Rajput, M.A.A; Funk, E; Borner, A; Hellwich, O

doi:10.1007/978-3-319-67876-4_17

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Boundless Reconstruction Using Regularized 3D Fusion

Rajput, M.A.A.; Funk, E.; Borner, A.; Hellwich, O.

Communications in Computer and Information Science 764: 359-378

2017

ISSN/ISBN: 1865-0929

DOI: 10.1007/978-3-319-67876-4_17
Accession: 106113705

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Summary

3D reconstruction from image based depth sensor is essential part of many offline or online robotic applications. Numerous techniques have been developed to integrate multiple depth maps to create 3D model of environment, however accuracy of the reconstructed 3D model exclusively depends upon the precision of depth sensing. Economical depth sensors such as Kinect and stereo camera sensors provide imprecise depth data which affect the integration process and produce unwanted noisy surfaces in 3D model. There exist several approaches which use image filtering based depth map denoising, however applying filtering directly on depth data can result in inconsistent and deformed 3D model. In this paper we investigate and extend a recursive variant of total variation based filtering to incorporate multi-view based depth images while applying implicit depth smoothing. Proposed framework uses sparse voxel representation to aid large scale 3D model reconstruction and is shown to reduce absolute surface error of final reconstructed 3D model by up, to 77% in comparison with state of the art 3D fusion techniques.