78 lines
1.6 KiB
Markdown
78 lines
1.6 KiB
Markdown
Documentation and developer's notes on voxels in IDEFIX package.
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Main concepts
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=============
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3D Images
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---------
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### Image referential system
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Similarly to 2D images of go referenced data, the 3D images coordinates system
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does not directly correspond to the one contained in the LiDAR point clouds.
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When comparing NumPy and geographic coordinate systems for 2D raters we can
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notice these orientations:
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```
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NumPy Geophic
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y
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+--> ^
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x| y|
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V +-->
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x
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Coordinate systems
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```
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For 3D data, the NumPy coordinate respect the right hand rule while the LiDAR
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*z* axis will directly correspond to altitude, thus breaking the right hand
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rule.
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IDEFIX silently project the data from one system to another while providing
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utility function to get back any projection or axis labels.
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Voxels data
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---
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- Point cloud arrangement
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+ **Density**
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+ Orientation PCA
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+ Normals
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- Feature distribution
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+ **Mean**: Reduce noise in the cell
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+ Variance: Characterize the distribution of the data
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+ **Mode**: Return the majority value (e.g. labels)
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+ Entropy
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+ *Min*: To create last echoes map
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+ Max
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+ Quantil
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Annexes
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=======
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Voxels glossary
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---
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- Step, resolution, bins...
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Algorithm
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---
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### Mode
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Insight and warn when use with high entropic data.
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- Slow (usual mode)
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- Quick (versus boost)
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Ideas
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---
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- Unified voxel format
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+ Do not store complete matrix
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* Point cloud like to benefit of PC IO for import/export.
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* Matrices always start to 0,0,0 image like, need metadata (start,
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steps, dtypes, field names...)
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+ Allow matrice aumgentation
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