Main concepts

3D Images

Image referential system

Similarly to 2D images of go referenced data, the 3D images coordinates system does not directly correspond to the one contained in the LiDAR point clouds.

When comparing NumPy and geographic coordinate systems for 2D raters we can notice these orientations:

NumPy       Geophic
  y      
 +-->         ^
x|           y|
 V            +-->
                x
 Coordinate systems

For 3D data, the NumPy coordinate respect the right hand rule while the LiDAR z axis will directly correspond to altitude, thus breaking the right hand rule.

IDEFIX silently project the data from one system to another while providing utility function to get back any projection or axis labels.

Voxels data

Point cloud arrangement
- Density
- Orientation PCA
- Normals
Feature distribution
- Mean: Reduce noise in the cell
- Variance: Characterize the distribution of the data
- Mode: Return the majority value (e.g. labels)
- Entropy
- Min: To create last echoes map
- Max
- Quantil

Annexes

Voxels glossary

Step, resolution, bins...

Algorithm

Mode

Insight and warn when use with high entropic data.

Slow (usual mode)
Quick (versus boost)

Ideas

Unified voxel format
- Do not store complete matrix
  - Point cloud like to benefit of PC IO for import/export.
  - Matrices always start to 0,0,0 image like, need metadata (start, steps, dtypes, field names...)
- Allow matrice aumgentation

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