About

PotreeConverterGPU generates 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 in corporation with the AIT-Austrian Institute of Technology.

Project Status

This project is a research project!

Feature	Status	Comment
LOD Generation on GPU	✅	Done
Exporting Potree data	✅	Done
Implicit random point subsampling	✅	Done
Explicit random point subsampling	✅	Done
Intra-Cell color filtering	✅	Done
Inter-Cell color filtering	✅	Done
Inter-Cell color filtering + distance weighting	✅	Done
Unit Tests	☑	In Progress...
Source code documentation	☑	In Progress...
Ply Import	❌	Only prepared binary files can be imported and processed
LAZ Import	❌	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/
CUDA	11.4	https://developer.nvidia.com/cuda-downloads
Prepared point cloud files	-	Downloads
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.

Project structure

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

Python tools

The tools/ folder contains the following Python 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: 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
Cxxopts	Command line parsing	https://github.com/jarro2783/cxxopts
Nlohmann	JSON library	https://github.com/nlohmann/json
Spdlog	Logging library	https://github.com/gabime/spdlog