The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 95–101, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-95-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 95–101, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-95-2019

  04 Jun 2019

04 Jun 2019

QUALITY PREDICTION OF DENSE POINTS GENERATED BY STRUCTURE FROM MOTION FOR HIGH-QUALITY AND EFFICIENT AS-IS MODEL RECONSTRUCTION

R. Moritani1, S. Kanai1, H. Date1, Y. Niina2, and R. Honma2 R. Moritani et al.
  • 1Graduate School of Information Science and Technology, Hokkaido University, Japan
  • 2Asia Air Survey Co., Ltd.

Keywords: Quality prediction, Dense Points, Structure from Motion, Bundle Adjustment, Multi-View Stereo, As-is model

Abstract. In this paper, we introduce a method for predicting the quality of dense points and selecting low-quality regions on the points generated by the structure from motion (SfM) and multi-view stereo (MVS) pipeline to realize high-quality and efficient as-is model reconstruction, using only results from the former: sparse point clouds and camera poses. The method was shown to estimate the quality of the final dense points as the quality predictor on an approximated model obtained from SfM only, without requiring the time-consuming MVS process. Moreover, the predictors can be used for selection of low-quality regions on the approximated model to estimate the next-best optimum camera poses which could improve quality. Furthermore, the method was applied to the prediction of dense point quality generated from the image sets of a concrete bridge column and construction site, and the prediction was validated in a time much shorter than using MVS. Finally, we discussed the correlation between the predictors and the final dense point quality.