From 5bcb591c746232226c488bc4553c315ff1278e23 Mon Sep 17 00:00:00 2001 From: Ravi Peters Date: Mon, 30 Sep 2024 11:14:34 +0200 Subject: [PATCH] doc improvements --- README.rst | 2 +- docs/getting_started.rst | 5 +---- docs/reconstruct_params.rst | 2 +- 3 files changed, 3 insertions(+), 6 deletions(-) diff --git a/README.rst b/README.rst index 630b388e..32cad45e 100644 --- a/README.rst +++ b/README.rst @@ -29,7 +29,7 @@ From 2022 onwards, `3DGI `_, a spinoff company from the aforeme This project has received funding from the European Research Council (ERC): - *2016-2022* under the European Unions Horizon2020 Research & Innovation Programme (grant agreement no. 677312 UMnD: Urban modelling in higher dimensions). - *2022-2024* under the Horizon Europe Research & Innovation Programme (grant agreement no. 101068452 3DBAG: detailed 3D Building models Automatically Generated for very large areas). -In *2024* this project has received funding from Kadaster, the Dutch Land Registry Office. +In *2024* this project has received funding from Kadaster, the Netherlands' Cadastre, Land Registry and Mapping Agency. Prior to 2024 the building reconstruction algorithms were developed as part of *Geoflow* and the *gfp-building-reconstruction plugin*. During the summer of 2024 the code was refactored and the *roofer* project was born. diff --git a/docs/getting_started.rst b/docs/getting_started.rst index 0d1068d7..8401f274 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -28,9 +28,6 @@ Clone this repository and use one of the CMake presets to build the roofer. # Optionally, install roofer cmake --install build -Usage ------ - Requirements on the input data ------------------------------ @@ -40,7 +37,7 @@ Point cloud + Acquired through aerial scanning, either Lidar or Dense Image Matching. But Lidar is preferred, because it is often of higher quality. Thus point clouds with only building facades eg. mobile mapping surveys are not supported. + The fewer outliers the better. + Classified, with at least *ground* and *building* classes. -+ Has sufficient point density. We achieve good results with 8-10 pts/m2 in the [3D BAG](https://3dbag.nl). ++ Has sufficient point density. We achieve good results with 8-10 pts/m2 in the `3D BAG `_. + Well aligned with the 2D building polygon. + Do include some ground points around the building so that the software can determine the ground floor elevation. + Pointcloud is automatically cropped to the extent of the 2D building polygon. diff --git a/docs/reconstruct_params.rst b/docs/reconstruct_params.rst index fdb7efaf..1e3ff48a 100644 --- a/docs/reconstruct_params.rst +++ b/docs/reconstruct_params.rst @@ -2,7 +2,7 @@ Reconstruction algorithm and parameters ======================================= .. image:: _static/img/algo-steps.png - :width: 400 + :width: 600 :alt: The roofer reconsruction algorithm The roofer building reconstruction algorithm is largely data-driven, so the quality of the result depends on the quality of the input data. The following parameters can be tuned to optimise the performance for a given point cloud.