GEMVS: a novel approach for automatic 3D reconstruction from uncalibrated multi-view Google Earth images using multi-view stereo and projective to metric 3D homography transformation

ABSTRACT

This paper proposes a novel approach for automatic 3D surface reconstruction from uncalibrated and multi-view Google Earth images by using a multi-view stereo method and 3D projective to metric transformation. Without the Rational Polynomial Coefficients, it is impossible to obtain the metric reconstruction of the 3D surface from multi-view satellite images. We solve the uncalibrated multi-view satellite image problem by employing a multi-view stereo vision technique followed by a projective to metric transformation. The virtual pose parameters of the satellite images are obtained by using COLMAP, and the virtual 3D projective reconstruction is done by using EnSoft3D. For projective to metric transformation, we propose to employ 3D homography transformation. Eight 3D correspondence pairs on the viewing frustums between the virtual reference camera and the ideal nadir camera are used to derive a 3D homography matrix. Using the 3D homography matrix, we finally obtain the metric reconstruction of 3D surface up to an unknown height scale in a reference coordinate system of the Google Earth desktop software. Experiments are done in several world locations on Google Earth including building and vegetation areas. Reconstruction error analysis with the Data Fusion Contest 19 dataset is also presented. The average of MAE and RMSE of five tile regions in the dataset are 1.596 m and 2.083 m, respectively.

KEYWORDS:

• Word
• multi-view stereo
• Google Earth
• surface reconstruction
• 3D DSM, RPC, stereo matching, 3D homography, projective reconstruction

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education [2021R1A6A1A03043144]; National Research Foundation of Korea(NRF) grant funded by Ministry of Science and ICT, South Korea [2021R1A2C2009722].