Command-Line Interface (CLI) application for efficient and optimized reconstruction of large-scale 3D building models.
GeoFlow is a software tool that can be used to automatically reconstruct 3D building models from point clouds, with a high level of detail. It is a powerful tool for creating detailed and accurate 3D models of buildings and can be used in a variety of applications. The software is fully automated, making it easy to use and efficient for large-scale projects.
Our program is based on GeoFlow and makes use of it to perform 3D reconstruction of buildings. The process is inspired by the 3D BAG project, which is an up-to-date dataset containing detailed 3D building models of the Netherlands, based on the official BAG data and national AHN point cloud. The optimization of the reconstruction process is achieved by indexing and tiling of the input data which reduces the processing time and resources needed to generate large-scale 3D building models. The indexing and tiling of both, 3D point cloud and 2D footprints, allows for more efficient processing and handling of the 3D reconstruction workflow.
The easiest way to install Optim3D on Windows is to use the binary package on the Release page. In case you can not use the Windows installer, or if you are using a different operating system, you can build everything from source.
NOTE: It is important to note that in order to use our program for 3D reconstruction of buildings, GeoFlow-bundle must be installed. Please read the License before using it.
After installation, you have a small program called optim3d. Use optim3d --help to see the detailed help:
Usage: optim3d [OPTIONS] COMMAND [ARGS]...
CLI tool to manage full optimized reconstruction of large-scale 3D
building models.
Options:
--help Show this message and exit.
Commands:
index2d QuadTree indexing and tiling of 2D building footprints.
index3d OcTree indexing of 3D point cloud using Entwine.
tiler3d Tiling of point cloud using the calculated processing areas.
reconstruct Optimized 3D reconstruction of buildings using GeoFlow.
post Post-processing generated CityJSON files.
The process consists of five distinct steps or commands that must be executed in a specific order to achieve the desired outcome.
Quadtree-based tiling scheme is used for spatial partitioning of building footprints. This assures that the reconstruction time per tile is more or less the same and that the tiles available for download are similar in file size. This is done using the first command index2d. Use optim3d index2d --help to see the detailed help:
Usage: optim3d index2d [OPTIONS] [FOOTPRINTS]
QuadTree indexing and tiling of building 2D footprints.
Options:
--output PATH Output directory. [default: ./output]
--osm <FLOAT FLOAT FLOAT FLOAT>...
Download and work with building footprints
from OpenStreetMap [west, north, est,
south].
--crs INTEGER Specify the Coordinate Reference System
(EPSG).
--max INTEGER Maximum number of buildings per tile.
[default: 3500]
--help Show this message and exit.
Processing large point cloud datasets is hardware-intensive. Therefore, it is necessary to index the 3D point cloud before processing. The index structure makes it possible to stream only the parts of the data that are required, without having to download the entire dataset. In this case, the spatial indexing of the airborne point cloud is performed using an octree structure. This can be easily done using Entwine, an open-source library for organizing and indexing large point cloud datasets using an octree data structure that allows fast and efficient spatial queries. This is done using the second command index3d. Use optim3d index3d --help to see the detailed help:
Usage: optim3d index3d [OPTIONS] POINTCLOUD
OcTree indexing of 3D point cloud using Entwine.
Options:
--output PATH Output directory. [default: ./output]
--help Show this message and exit.
The tiling of the indexed point cloud is based on processing areas calculated when the footprints were indexed. This is achieved using the third command tiler3d. Use optim3d tiler3d --help to see the detailed help:
Usage: optim3d tiler3d [OPTIONS]
Tiling of 3D point cloud using the calculated processing areas.
Options:
--areas PATH The calculated processing areas. [default:
./output/processing_areas.gpkg]
--indexed PATH Indexed 3D point cloud directory. [default:
./output/indexed_pointcloud]
--output PATH Output directory. [default: ./output]
--help Show this message and exit.
The 3D reconstruction of building models is performed in this step. The process make use of GeoFlow to generate hight detailed 3D building models tile by tile. This is achieved using the fourth command reconstruct. Use optim3d reconstruct --help to see the detailed help:
Usage: optim3d reconstruct [OPTIONS]
Optimized 3D reconstruction of buildings using GeoFlow.
Options:
--pointcloud PATH 3D point cloud tiles directory. [default:
./output/pointcloud_tiles]
--footprints PATH 2D building footprints tiles directory. [default:
./output/footprint_tiles]
--output PATH Output directory. [default: ./output]
--help Show this message and exit.
The generated CityJSON files should be processed to add information about tiles to 3D objects. This is done using the fifth command post. Use optim3d post --help to see the detailed help:
Usage: optim3d post [OPTIONS]
Postprocess the generated CityJSON files.
Options:
--cityjson PATH CityJSON files directory. [default:
./output/model/cityjson]
--help Show this message and exit.
The results of each command are saved in the output folder, which will look like this after executing all the commands:
├── output
│ ├── flowcharts
│ │ ├── *.json
│ ├── footprint_tiles
│ │ ├── *.cpg
│ │ ├── *.dbf
│ │ ├── *.prj
│ │ ├── *.shp
│ │ ├── *.shx
│ ├── indexed_pointcloud
│ │ ├── ept-data
│ │ │ ├── *.laz
│ │ ├── ept-hierarchy
│ │ │ ├── 0-0-0-0.json
│ │ ├── ept-sources
│ │ │ ├── *.json
│ │ ├── ept.json
│ │ ├── ept-build.json
│ ├── model
│ │ ├── cityjson
│ │ ├── *.city.json
│ │ ├── obj
│ │ ├── *.obj
│ │ ├── *.obj.mtl
│ ├── pointcloud_tiles
│ │ ├── *.las
│ ├── processing_areas.gpkg
│ └── quadtree.gpkgThe 3D building models can be viewd using Ninja, the official web viewer for CityJSON files.
Automatic correction of buildings ground floor elevation in 3D City Models
GeoFlow requires that the point cloud includes some ground points around the building so that it can determine the ground floor elevation. However, for aerial point clouds, buildings surrounded by others may not meet this condition which may result in inaccurate height estimation above the ground. This can be resolved using ZRect3D, a tool for automatic correction of buildings ground-floor elevation in CityJSON files using ground points from LiDAR data.
This software is under the BSD 3-Clause "New" or "Revised" license which is a permissive license that allows you almost unlimited freedom with the software so long as you include the BSD copyright and license notice in it. Please read the LICENSE and the COPYING files for more details.
This software was developped by Anass Yarroudh, a Research Engineer in the Geomatics Unit of the University of Liege. For more detailed information please contact us via [email protected], we are pleased to send you the necessary information.