Multiscale Feature Model for Terrain Data Based on Adaptive Spatial Neighborhood

Table 2

Experiment result of the quantity of data load in different frames when screen error is 2.5 pixels.

Data source	1st frame		10th frame		100th frame		1034th frame
	Loaded triangles	Schedule time (s)	Loaded triangles	Schedule time (s)	Loaded triangles	Schedule time (s)	Loaded triangles	Schedule time (s)
GTOPO30	19,661	0.04	1,967	0.010	1,804	0.009	1,924	0.010
Jilin province	76,022	0.16	4,842	0.050	4,947	0.052	4,859	0.050
Zhujiang	18,678	0.02	2,241	0.010	2,306	0.015	2,598	0.015
Tian-chi	85,289	0.10	5,063	0.070	49,040	0.067	5,813	0.720
Colorado Grand Canyon	83,886	0.09	5,381	0.030	5,412	0.035	5,569	0.035
Seattle	319,816	0.65	10,231	0.400	10,059	0.390	10,952	0.420
Yakima	325,059	0.60	10,565	0.360	10,884	0.370	10,237	0.340
Crater Lake	309,330	0.61	10,967	0.390	10,932	0.390	11,098	0.410
Puget Sound	85,197	0.15	4,568	0.069	4,623	0.069	4,754	0.070
Mount Rainier	83,886	0.12	4,922	0.060	4,867	0.053	4,733	0.051