Large-Scale and Drift-Free Surface Reconstruction Using Online Subvolume Registration

Nicola Fioraio, Jonathan Taylor, Andrew Fitzgibbon, Luigi Di Stefano, Shahram Izadi; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 4475-4483

Abstract


Depth cameras have helped commoditize 3D digitization of the real-world. It is now feasible to use a single Kinect-like camera to scan in an entire building or other large-scale scenes. At large scales, however, there is an inherent challenge of dealing with distortions and drift due to accumulated pose estimation errors. Existing techniques suffer from one or more of the following: a) requiring an expensive offline global optimization step taking hours to compute; b) needing a full second pass over the input depth frames to correct for accumulated errors; c) relying on RGB data alongside depth data to optimize poses; or d) requiring the user to create explicit loop closures to allow gross alignment errors to be resolved. In this paper, we present a method that addresses all of these issues. Our method supports online model correction, without needing to reprocess or store any input depth data. Even while performing global correction of a large 3D model, our method takes only minutes rather than hours to compute. Our model does not require any explicit loop closures to be detected and, finally, relies on depth data alone, allowing operation in low-lighting conditions. We show qualitative results on many large scale scenes, highlighting the lack of error and drift in our reconstructions. We compare to state of the art techniques and demonstrate large-scale dense surface reconstruction "in the dark", a capability not offered by RGB-D techniques.

Related Material


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[bibtex]
@InProceedings{Fioraio_2015_CVPR,
author = {Fioraio, Nicola and Taylor, Jonathan and Fitzgibbon, Andrew and Di Stefano, Luigi and Izadi, Shahram},
title = {Large-Scale and Drift-Free Surface Reconstruction Using Online Subvolume Registration},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}