Fig. 1. Flow chart of RANSAC

Fig. 2. Five typical examples of RANSAC hypothesis evaluation

Fig. 3. Flow chart of triangle voting

Fig. 4. Partial point cloud visualization of the experimental datasets. (a) U3M dataset; (b) BoD5 dataset; (c) BMR dataset

Fig. 5. The robustness of the metrics to parameter t in different datasets. (a) U3M dataset; (b) BoD5 dataset; (c) BMR dataset改变数据集时不同假设度量对参数的鲁棒性。(a) U3M数据集；(b) BoD5数据集；(c) BMR数据集

Fig. 6. The robustness of the metrics to parameter t at low thresholds in different datasets. (a) U3M dataset; (b) BoD5 dataset; (c) BMR dataset不同数据集下不同假设度量在低阈值时对参数的鲁棒性。(a) U3M数据集；(b) BoD5数据集；(c) BMR数据集

Fig. 7. The robustness of the metrics to parameter when varying the number of RANSAC iterations. (a) 600 iters; (b) 800 iters; (c) 1000 iters不同RANSAC迭代次数时不同假设评估度量对参数的鲁棒性。(a) 600次迭代；(b) 800次迭代；(c) 1000次迭代

Fig. 8. The robustness of the metrics to parameter when varying RMSE thresholds . (a) ; (b) ; (c) ; (d) 改变RMSE阈值时不同假设度量对参数的鲁棒性。(a) ; (b) ; (c) ; (d)

Fig. 9. Comparison between the metrics considered with and without the parameter (w:with，wo:without)参数的引入与否对不同假设度量的影响

Fig. 10. The impact of the size of the initial correspondence set on different hypothesis evaluations初始对应点集的大小对不同假设度量的影响