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Table 2: Terrain and surface characteristics datasets for Earth and Mars used by the OMEGA grid generator.
2002
Cited by 1
Table 1. Performance results of three terrain features
2004
"... In PAGE 20: ...unning time was 1.14 hours (4104 seconds) and 57.6 percent of the surface was visible. lt; Table1 about here gt; None of the features can match the coverage of the exhaustive search. The best are the peaks, with 46.... ..."
Cited by 2
Table 3. The solution and computing time results of the visibility heuristics for all the three terrain features
2004
"... In PAGE 25: ... For comparison, we estimate that same algorithms using all 1600 pixels as potential candidates, would take 56 hours to solve the 10 observer case. As the results show ( Table3 ) the SA heuristic produces the best solution in almost all cases, although the differences between the visible areas produced by each heuristic are not large. However, there is a marked difference in the computing time required by the methods.... In PAGE 25: ... This also emphasises the computationally tractable benefit of Swap algorithm on the terrain features because the algorithm completes ten optimal locations problem within 20 minutes compared to the estimated 56 hours for the run on the entire surface area search case (total 1600 candidates). lt; Table3 about here gt; The heuristics produce similar results in terms of overall coverage, but it is also ... ..."
Cited by 2
Table 3.6 provides more evidence for the above conclusion. In this table we see the average simplification size (based on our input terrains and tests) that is needed in order to achieve a given quality. For example, consider the column corresponding to quality 0.970. While the average over the sizes of the minimum size simplifications generated by our method that achieve this quality is only 250, this average for the other methods is larger; it is 315 for Terra, 550 for QSlim, and 1110 for GcTin. During these experiments, we kept track of the maximum vertical distance (along the z-axis) between a vertex of the input terrain and the correspond- ing point on the surface of the simplification. We found that VPTS resulted in much larger vertical distances, even in those cases in which our simplifica- tion is substantially better than the others according to the transmitter-receiver measure. This result is to be expected, since our method was designed to mini- mize the transmitter-receiver measure, while the other methods were designed to minimize vertical distance error.
2005
Table 1: Vegetation filtering of the raw data // Res. Size # of vertices Time Mesh
2003
"... In PAGE 4: ... The vegetation points are plotted in color while the ground points are in white. Table1 con- tains statistics about the results of the vegetation filtering for this example. One added difficulty in this example is that, after fil- tering the vegetation in the robot data, the terrain surface contains a number of empty areas with no data, termed range shadows .... ..."
Cited by 17
Table 5: Test results CRAB
"... In PAGE 6: ...ighting each other in certain positions. Both phenomena are caused by kinematical constraints. In the interval between the peaks the currents descend significantly because rolling resistance is the only force opposing the movement. In Table5 , the summary of the tests performed with the CRAB is presented. The CRAB always succeeded climbing the obstacle on both types of terrain surface and with both types of controller.... ..."
Table 1. Terrain complexity
2002
"... In PAGE 4: ...ay tracer of Parker et. al. [13] to render two different terrain models, which are called Hogum and Range-400. Images of these data sets are given in Figure 2 and their geo- metric complexity is given in Table1 . The intersection routine is similar to that used for isosurface rendering [14].... ..."
Cited by 7
Table 1: Constants A and B de ning the ECMWF hybrid coordinates. The last two columns give the pressure and the distance from the surface of the earth for psfc = 1013 and the given temperature pro le.
"... In PAGE 2: ...1 The 31-layer ECMWF hybrid coordinate system ECMWF[5] developed a hybrid coordinate system, which is terrain-following at the surface of the earth and has pressure levels at the top of the model. This coordinate system is induced by the surface pressure psfc, given at xed time intervals tmeteo, and by the functions A and B given at the vertical layer boundaries (see Table1 ). The pressure at the vertical grid points k+1 2 (cf.... In PAGE 2: ...8) can then be expressed by Z l?12 l+1 2 r d = ? Z pl?1 2 pl+12 T p dp Tl(tmeteo) log pl+12 (tmeteo) pl?12 (tmeteo) ! : (1.9) In Table1 we give the vertical distances from the surface of the earth for psfc = 1013 and a given temperature pro le. 1.... ..."
Table 1. Participants of the eye tracking experiment
"... In PAGE 7: ... height is applied together with the shading effects that take account of the original geometry of the terrain surface. In order to confirm these effects, we asked six participants (See Table1 ) to extract infor- mation on driving routes through the navigating frames generated by our system, and tracked their eye movements as shown in Figure 13(a). Here, the participants are requested to see perspective animation clips that guide the Takeshi village and Hida highway first, and then the corresponding nonperspective clips without being informed of the dif- ference between two animation clips.... ..."
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