Terrain surface from drone data

After adapting the algorithm dedicated to the extraction of digital terrain model (DTM) from terrestrial LiDAR point cloud, it is possible to process point clouds generated from imagery by photogrammetry software. Here is an example of DTM coming from drone data:

Pocket Air Quality

Pocket Air Quality is a new Android app that collects air quality information online and presents  different pollutants concentrations and the overall air cleanliness at the closest station. This side project has been done fully using the framework Ionic and is available for free on the Google Play Store The data comes from The World... Continue Reading →

High accuracy terrain models

This method finely approximates the ground surface by relying on deep learning to separate vegetation from potential ground points, files holes by blending multiple local approximations through partition of unity principle, then improves the accuracy by pushing the surface towards the data points through an iterative convection model.

Non photo-realistic rendering with OpenGL ES

Inspired by the EDL shading technique and SSAO, I implemented a new shader for OpenGL ES designed to enhance the rendering of point clouds. As the shading function rely only on the projected depth, and because the variable gl_FragDepth is missing in OpenGL ES, a workaround was to implement a custom depth buffer using a... Continue Reading →

3D Poisson surface model of trees

Departing from traditional tree models made of cylinders (in green), sharper models (in blue) can be computed. Expressed as a continuous surface, they describe the shape better and allow to assess precisely the tree properties. The 3D samples and the surface model of a tropical tree can be visualize here

Terrestrial LiDAR scan processing

Sequence of processing steps: modelling of the terrain surface, detection of the tree stem, then estimation of the tree model. 3D model of a Mangrove tree (Avicennia germinans Copyright A. Olagoke and C. Proisy, 2014, IRD UMR AMAP)

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