Fig. 1. Image matching at distance ratio threshold of 0.8^[27]. (a) 1m2; (b) 1m3; (c) 1m4; (d) 1m5

Fig. 2. Histograms of distance ratio of matching images. (a) 1m2; (b) 1m3; (c) 1m4; (d) 1m5

Fig. 3. Distribution of inliers and outliers in the matched images with zoom change^[28]. (a) Correct matched pairs; (b) distribution of inliers and outliers (red points are outliers, yellow points are inliers)

Fig. 4. Secondary removal of outliers^[29]. (a) Matched pairs obtained by existing method; (b) vectors of position change between inliers, and the included outliers

Fig. 5. Flow chart of the proposed method

Fig. 6. Schematic of conscient distribution of neighbor points

Fig. 7. Algorithm of inlier ratio promotion based on near neighbor inlier distribution consensus

Fig. 8. Histograms of inlier distance errors. (a) Boston; (b) BruggeTower; (c) ExtremeZoom; (d) Graf; (e) Effel

Fig. 9. Multi-model estimation algorithm based on distance error marginalization

Fig. 10. Vector of position change between inliers. (a) Leafs^[28]; (b) Toy and Bread^[29]; (c) Booksh^[28]; (d) ExtremeZoom^[28]

Fig. 11. Schematic of image gridding and local area with its neighbor areas

Fig. 12. Secondary removal algorithm of outlier

Fig. 13. Experimental results on homogr dataset. (a) Inlier ratio of different image pairs; (b) inlier ratio after outlier filtering out; (c) recall of inliers; (d) original number of matched pairs; (e) number of matched pairs after outlier filtering out

Fig. 14. Inlier distance error obtained by different methods

Fig. 15. Comparison of average performance of different algorithms on different criteria. (a) Undetected outlier ratio; (b) number of inliers; (c) consumption time

Fig. 16. Multi-plane estimation obtained by the proposed method under Adelaidermf data set. (a) ladysymon; (b) neem; (c) nese; (d) johnsona; (e) elderhallb; (f) unihouse; (g) bonhall; (h) napiera; (i) oldclassicswing; (j) library