 Experimental results show the competitive advantage of MRP interpolation on real-world data, as the average performance of SD-MRP on real-world data under all experimental conditionswas ranked significantly higher than that of all other methods, followed by WP-MRP. On synthetic data, we show that WP-MRP can perform better than SD-MRP given sufficiently informative features. We further found that, even in cases where our methods had no significant advantage over baselines numerically, our methods preserved the spatial structure of the target grid better than the baselines .
<meaning-changed>  Experimental results show the competitive advantage of MRP interpolation on real-world data, as the average performance of SD-MRP on real-world data under all experimental conditionswas ranked significantly higher than that of all other methods, followed by WP-MRP. On synthetic data, we show that WP-MRP can perform better than SD-MRP given sufficiently informative features. We further found that, even in cases where our methods had no significant advantage over baselines numerically, our methods preserved the spatial structure of the target grid better than the baselines .
 Experimental results show the competitive advantage of MRP interpolation , which achieved significantly lower errors than the existing methods in 23 out of 40 experimental conditions, or 35 out of 40 when including O-MRP .