Update README.md

Author: td43

README.md

@@ -1,50 +1,101 @@
 # upsamplingCloudPCL
-Upsampling method for an input cloud using mls method of PCL 1.9.1
+Upsampling method for an input cloud using [MovingLeastSquares](https://pointclouds.org/documentation/classpcl_1_1_moving_least_squares.html) method of PCL
 
 ## Input file structure support
 
-* .pcd 
-* .ply
-* .txt
-* .xyz
+| Format      | Description |
+| ----------- | ----------- |
+| .pcd      | Point Cloud Data file format       |
+| .ply   | Polygon file format        |
+| .txt   | Text file format        |
+| .xyz      | X Y Z Text file format       |
 
 ## Output file structure (.pcd)
 
 * unsampled_cloud.pcd 
 
 ## Example
-<img src="./example/example.png" align="center" height="400" width="720"><br>
+<p align="center">
+   <img src="./example/example.png"><br>
+</p>
+
+<p align="center">
+   <img src="https://user-images.githubusercontent.com/35694200/183946061-12df0269-fcc1-4fa3-a635-c2a86d5ba879.png"><br>
+</p>
+
+<p align="center">
+   <img src="https://user-images.githubusercontent.com/35694200/183946790-f34f6129-6e21-4d4c-bd1c-1066c630943b.png"><br>
+</p>
+
+
+## Command line
+```cpp
+Usage: ./upsampling_cloud [options] 
+
+Optional arguments:
+-h --help         	shows help message and exits [default: false]
+-v --version      	prints version information and exits [default: false]
+--cloudfile       	input cloud file [required]
+--search-radius   	epsilon value [default: 0.03]
+--sampling-radius 	epsilon value [default: 0.005]
+--step-size       	epsilon value [default: 0.005]
+-o --output-dir   	output dir to save clusters [default: "-"]
+-d --display      	display clusters in the pcl visualizer [default: false]
+```
+
+## Dependencies
+This projects depends on the Point Cloud Library (it works with version `1.8...1.12.1`) and its dependencies.
+|     Package      |   Version      |                             Description                                                                                                                  |
+|     -----------       |   -----------      |                                  -----------                                                                                                                     |
+|        VTK           |    9.0.0          |   Visualization toolkit                                                           |
+|        PCL           |     1.12.1       |                    The Point Cloud Library (PCL)                                            |
+|        Eigen        |     3.7.7         |  Eigen is a library of template headers for linear algebra                                 |
+|        Flann        |     1.9.1         |      Fast Library for Approximate Nearest Neighbors                                  |
+|       Boost         |    1.77.0        | Provides support for linear algebra, pseudorandom number generation, multithreading      |
+|       OpenGL      |     21.2.6       | Programming interface for rendering 2D and 3D vector graphics.                    |
+
 
--------------------
 ## Compilation
-* Set "YOUR OWN" PCL Build DIR in CMakeList.txt e.g: **/opt/pcl-1.9.1/build** and save it.
-* Create a "build" folder
+### Compile from source
+
+1. Download source code
+
+```bash
+git clone https://github.com/danielTobon43/upsamplingCloudPCL
+```
 
-in the main folder:
+2. Create a "build" folder at the top level of the upsamplingCloudPCL
 
-    - cd build  
-    - cmake ../src/
-    - make
-       
+```bash
+cd upsamplingCloudPCL/ && mkdir build
+```
+
+3. Compile with CMake
+
+```bash
+cd build/ && cmake ../ && make
+```      
         	 
 ### Test
+```bash
+cd /build
+./upsampling_cloud --cloudfile <path/to/cloud-file>
+```
 
-    cd /build
-    ./upsampling_cloud <cloud file> 
-    
 ## Note
 
-You can modify the parameters to obtain better results 
-
-    mls.setComputeNormals(true);
-    mls.setInputCloud(input_cloud);
-    mls.setSearchMethod(kd_tree);
-    mls.setSearchRadius(search_radius);
-    mls.setUpsamplingMethod(pcl::MovingLeastSquares<pcl::PointXYZRGB, pcl::PointXYZRGB>::UpsamplingMethod::SAMPLE_LOCAL_PLANE);
-    mls.setUpsamplingRadius(sampling_radius);
-    mls.setUpsamplingStepSize(step_size);
-    mls.setPolynomialOrder(pol_order);
-    mls.setSqrGaussParam(gauss_param);// (the square of the search radius works best in general)
-    mls.setCacheMLSResults(true);//Set whether the mls results should be stored for each point in the input cloud.
-    mls.setNumberOfThreads(num_threats);
+You can modify the parameters to obtain better results [here](https://github.com/danielTobon43/upsamplingCloudPCL/blob/master/src/main.cpp#:~:text=void%20upsampling(pcl,Ptr%26%20output_cloud)%20%7B)
 
+```cpp
+mls.setComputeNormals(true);
+mls.setInputCloud(input_cloud);
+mls.setSearchMethod(kd_tree);
+mls.setSearchRadius(search_radius);
+mls.setUpsamplingMethod(pcl::MovingLeastSquares<pcl::PointXYZRGB, pcl::PointXYZRGB>::UpsamplingMethod::SAMPLE_LOCAL_PLANE);
+mls.setUpsamplingRadius(sampling_radius);
+mls.setUpsamplingStepSize(step_size);
+mls.setPolynomialOrder(pol_order);
+mls.setSqrGaussParam(gauss_param);// (the square of the search radius works best in general)
+mls.setCacheMLSResults(true);//Set whether the mls results should be stored for each point in the input cloud.
+mls.setNumberOfThreads(num_threats);
+```