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Generate Potree compatible LOD data from 3D point clouds on the GPU using CUDA

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# About

PotreeConverterGPU generates [Potree](https://github.com/potree/potree/) compatible LOD data structures from point clouds entirely on 
the GPU using CUDA.

The project is part of a master's thesis with the title *In-core level-of-detail generation for point clouds
on GPUs using CUDA* and is conducted by the [Technical Universe of Vienna](https://www.cg.tuwien.ac.at/research/projects/Scanopy/) 
in corporation with the [AIT-Austrian Institute of Technology](https://www.ait.ac.at/en/).

# Project Status
This project is a research project!

| Feature							              | Status    | Comment                                                   |
|-------------------------------------------------|-----------|-----------------------------------------------------------|
| LOD Generation on GPU				              | &#9989;   | Done                                                      |
| Exporting Potree data				              | &#9989;   | Done                                                      |
| Implicit random point subsampling               | &#9989;   | Done                                                      |
| Explicit random point subsampling	              | &#9989;   | Done                                                      |
| Intra-Cell color filtering		              | &#9989;   | Done                                                      |
| Inter-Cell color filtering		              | &#9989;   | Done                                                      |
| Inter-Cell color filtering + distance weighting | &#9989;   | Done                                                      |
| Unit Tests						              | &#9745;   | In Progress...                                            |
| Source code documentation			              | &#9745;   | In Progress...                                            |
| Ply Import						              | &#10060;  | Only prepared binary files can be imported and processed  |
| LAZ Import						              | &#10060;  | Only prepared binary files can be imported and processed  |

# Release version
Be aware that the master branch is constantly updated. 
Therefore you should checkout or download release versions.
This releases also contain necessary input files (morrowbay.bin, heidentor.bin, coin.bin) .

# Getting started

## Building from source

### Prerequirements

| Name                            | Minimum Version   | Link                                                                                        |
| --------------------------------|-------------------| --------------------------------------------------------------------------------------------|
| CMAKE                           | 3.10              | [https://cmake.org/](https://cmake.org/)                                                    |
| CUDA                            | 11.4              | [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads)  |
| Prepared point cloud files      | -                 | [Downloads](http://www.dreamcoder.at/potree/download.html)                                  |
| c++ 17 compiler                 | -                 | -                                                                                           |

### Building instructions

```
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ../
cmake --build . --config Release
```

**Note**:

* ``-DENABLE_KERNEL_TIMINGS=ON`` enables CUDA kernel timings (performance drawback)
* ``-DENABLE_KERNEL_ERROR_CHK=ON`` performs full error check after each CUDA kernel (performance drawback)

## Running PotreeConverterGPU

Put the binary file(s) in the same folder as the PotreeConverterGPU executable and run the program with the following
commands:

* morrowbay.bin

``PotreeConverterGPU.exe -f morrobay.bin -o .\output -p 119701547 -d 27,0.01 -t double -g 512,128 -i``

* heidentor.bin

``PotreeConverterGPU.exe -f heidentor.bin -o .\output -p 25836417 -d 15,0.001 -t float -g 512,128 -i``
* coin.bin

``PotreeConverterGPU.exe -f coin.bin -o .\output -p 5138448 -d 15,0.001 -t float -g 512,128 -i``

### Program Arguments
```
Usage:
  PotreeConverterGPU [OPTION...]

-f, --file arg              File name point cloud
  -a, --averaging-intra       Apply intra-cell color averaging
  -i, --averaging-inter       Apply inter-cell color averaging
  -w, --weight                Apply a weighting function during inter-cell color filtering
  -r, --random                Perform Explicit-Random-Subsampling, otherwise Implicit-Random-Subsampling is applied
  -o, --output arg            Output path for the Potree data
  -p, --points arg            Point amount of the cloud
  -t, --type arg              The datatype of the cloud coordinates: "float" / "double"
  -d, --data arg              Data infos for stride and scale: [float, float]
  -g, --grids arg             Grid sizes for chunking and subsampling: [int, int]
  -m, --merge_threshold arg   The merging threshold (default: 10000)
  -e, --estimated_output arg  The estimated output point amount factor (default: 2.2)
  -h, --help                  Print usage
```

### Output
PotreeConverterGPU generates the following output files:

| Filename                  | Description                                                                           | 
| --------------------------|---------------------------------------------------------------------------------------|
| hierarchy.bin             | Contains information about octree nodes in binary form (required by Potree)           | 
| memory_report.html        | A diagram which shows the total GPU memory consumption per cudamalloc and cudafree    |
| metadata.json             | Octree metadata and data description (required by Potree)                             |
| octree.bin                | The binary lod cloud data (required by Potree)                                        |
| point_distribution.html   | A diagram which shows the point distribution in the leaf nodes                        |
| statistics.json           | Octree related statistics and information

The resulting data can be directly rendered using [PotreeDesktop](https://github.com/potree/PotreeDesktop).

# Project structure
-  [External/](External/) contains external tools and libraries
-  [OctreeApi/](OctreeApi/) Exposes the functionality from the OctreeLibrary using an C-API. The API is compiled to a shared library.
-  [OctreeApi/OctreeLibrary/](OctreeApi/OctreeLibrary) contains the actual LOD generation logic and CUDA kernels. It is compiled to a static library.
-  [src/](src/) contains the PotreeConverterGPU project. This project builds an executable and links to the OctreeApi.

# Python tools
The [tools/](tools/) folder contains the following Python tools: 
-  [potree_to_ply.py](tools/potree_to_ply.py): a tool for converting Potree 2.0 data generated with PotreeConverterGPU into PLY files. A single PLY 
   file is generate per LOD. An additional PLY file containing the content of all leaf nodes is also created.
-  [memory_consumption.ply](tools/memory_consumption.py): a tool for estimating the minimum required GPU memory space to process a point cloud with
   a specific amount of points.

# External Tools/Libraries
| Library           | Description               | Link                                      |
| ------------------|---------------------------|-------------------------------------------------------------------------------|
| Catch2            | Unit Testing framework    | [https://github.com/catchorg/Catch2](https://github.com/catchorg/Catch2)      |
| Cxxopts           | Command line parsing      | [https://github.com/jarro2783/cxxopts](https://github.com/jarro2783/cxxopts)  |
| Nlohmann          | JSON library              | [https://github.com/nlohmann/json](https://github.com/nlohmann/json)          |
| Spdlog            | Logging library           | [https://github.com/gabime/spdlog](https://github.com/gabime/spdlog)          |

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