Isometric Results - IMPLEMENTATION AND RESULTS

CHAPTER 5 IMPLEMENTATION AND RESULTS

5.1 Isometric Results

The input data in Fig. 5.1(b) (case_1) is stochastic and marble-like texture. It only contains two kinds of colors, and it is vivid. It is information-rich that only needs small amount of data to represent the whole texture. It means that we can synthesize larger results (bigger than two times of input data size) with this kind of textures. Fig. 5.1(c)~(f) show the result. As we can see, the result is continuous and not the duplication of the input data.

The input data in Fig. 5.2(b) (case_2) is particle-like texture. It contains few kinds of color, and it is very different between particles and background. The particles in case_2 are the same kind. As long as there are few complete particle patterns in the input data, we can synthesize good result, as shown in Fig.

5.2(c)~(f). Because the few particle patterns can represent whole texture, we can synthesize it from 32×32×32 to 128×128×128, even from 64×64×64 to 256×256×256 volume data (the result size is four times of input size).

The input data in Fig. 5.3(b) (case_3) is another type of particle texture. It contains different sizes and different colors of particles, and most of particles look like the same color as background. Fig. 5.3(c)~(f) show the result from

64×64×64 to 128×128×128 volume result. Because the input data is not information-rich, the distribution of the particles in the result is sparse, not as it in the input data. It means that the size of the input data is not enough to contain enough information for us to synthesize.

The input data in Fig. 5.4(b) (case_4) is about sea water. It is a kind of homogeneous textures because it is almost in the same color in the whole volume.

The main feature in the input data is the highlight area. As the result in Fig. 5.4(c)~(f) shown, there are few highlight area in the result volume data.

The input data in Fig. 5.5(b) (case_5) is a kind of structural textures. The patterns in the input data are small and compact, so the texture is information-rich. Only small size for input data could contain enough patterns for synthesis. It can be synthesized with a few input data and obtain good results.

The result is shown in Fig. 5.5(c)~(f).

The input data in Fig. 5.6(b) (case_6) is structural and continuous on two directions and broken on the other direction. It is consist of thin stokes from black and white. The result in Fig. 5.6(c)~(f) is good at the two continuous directions, which is continuous and not duplicate, but broken at the other direction because the input data is information-poor.

The input data in Fig. 5.7(b) (case_7) is structural with bigger patterns. The feature in the input data is too large, so the input data could not represent the whole volume data if the input size is too small. As we can see in Fig. 5.7(c)~(f), the information in the 64×64×64 input data is regular, not various, so the result is

not as we expect.

Table 5.1 computation time for different textures

Feature Vector

Construction

Similarity Set Construction

Synthesis Process Case_1 47.8 seconds 85 hours 37 minutes 7 hours 55 minutes Case_2 31.1 seconds 85 hours 28 minutes 8 hours 17 minutes Case_3 35.4 seconds 86 hours 5 minutes 8 hours 22 minutes Case_4 48.6 seconds 84 hours 42 minutes 8 hours 5 minutes Csse_5 34.3 seconds 83 hours 46 minutes 7 hours 28 minutes Csse_6 38.2 seconds 86 hours 21 minutes 8 hours 41 minutes Csse_7 35.7 seconds 86 hours 51 minutes 8 hours 43 minutes Csse_8 37.3 seconds 93 hours 30 minutes 6 hours 52 minutes Csse_9 34.9 seconds 85 hours 4 minutes 8 hours 43 minutes

(a) (b)

(e) (f) Figure 5.1 Input and result data for case_1

(a) cross sections at X=32, Y=32, and Z=32 for input data (b) input volume data for case_1

(c) cross section at X=126, Y=126, and Z=126 for result data (d) cross section at X=80, Y=80, and Z=80 for result data (e) cross section at X=64, Y=64, and Z=64 for result data (f) result volume data for case_1

(a) (b)

(e) (f) Figure 5.2 Input and result data for case_2

(a) cross sections at X=32, Y=32, and Z=32 for input data (b) input volume data for case_2

(a) (b)

(e) (f) Figure 5.3 Input and result data for case_3

(a) cross sections at X=32, Y=32, and Z=32 for input data (b) input volume data for case_3

(a) (b)

(e) (f) Figure 5.4 Input and result data for case_4

(a) cross sections at X=32, Y=32, and Z=32 for input data (b) input volume data for case_4

(a) (b)

(e) (f) Figure 5.5 Input and result data for case_5

(a) cross sections at X=32, Y=32, and Z=32 for input data (b) input volume data for case_5

(a) (b)

(e) (f) Figure 5.6 Input and result data for case_6

(a) cross sections at X=32, Y=32, and Z=32 for input data (b) input volume data for case_6

(a) (b)

(e) (f) Figure 5.7 Input and result data for case_7

(a) cross sections at X=32, Y=32, and Z=32 for input data (b) input volume data for case_7

在文檔中具有向量場控制功能的實體材質合成 (頁 37-47)