Analyze_Intermediate_Images

Overview

Step1 :

Create ensemble point clouds(.spbr) from one input point cloud

$ ./analyzeIntermediateImages [input_file] [output_path]

Input repeat level (the default repeat level is 1) : 10
Num. of input points : 1122011
Repeat level         : 10
Num. of points in each ensemble : 112201

Shuffled.
 ensemble1.spbr done.
 ensemble2.spbr done.
 ensemble3.spbr done.
 ensemble4.spbr done.
 ensemble5.spbr done.
 ensemble6.spbr done.
 ensemble7.spbr done.
 ensemble8.spbr done.
 ensemble9.spbr done.
 ensemble10.spbr done.

File export of all ensembles is complete.

Step2 :

Automatically, snapshot all intermediate images by using spbr_auto_snap

$ python spbr_continuously.py [spbr_file_path] [spbr_header_file] [repeat_level]

Step3 :

Calculate variance(standard deviation) for each corresponding pixels

$ python calc_variance_for_each_pixel.py [input_images_path] [repeat_level] [image_resolution]

Result

Intermediate image (L=100)

Coords Noise	Color Noise

Original point cloud (L=1)

Coords Noise	Color Noise

Standard deviation image and histogram

Coords Noise	Color Noise

Transition of standard deviation when increasing repeat level

Coords Noise	Color Noise

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M_mean and M_max	M/L