# filters.poisson¶

The poisson filter passes data Mischa Kazhdan’s poisson surface reconstruction algorithm. [Kazhdan2006] It creates a watertight surface from the original point set by creating an entirely new point set representing the imputed isosurface. The algorithm requires normal vectors to each point in order to run. If the x, y and z normal dimensions are present in the input point set, they will be used by the algorithm. If they don’t exist, the poisson filter will invoke the PDAL normal filter to create them before running.

The poisson algorithm will usually create a larger output point set than the input point set. Because the algorithm constructs new points, data associated with the original points set will be lost, as the algorithm has limited ability to impute associated data. However, if color dimensions (red, green and blue) are present in the input, colors will be reconstruced in the output point set.

[Kazhdan2006] | Kazhdan, Michael, Matthew Bolitho, and Hugues Hoppe. “Poisson surface reconstruction.” Proceedings of the fourth Eurographics symposium on Geometry processing. Vol. 7. 2006. |

This integration of the algorithm with PDAL only supports a limited set of the options available to the implementation. If you need support for further options, please let us know.

## Example¶

```
{
"pipeline":[
"dense.las",
{
"type":"filters.poisson",
},
{
"type":"writers.ply",
"filename":"isosurface.ply",
}
]
}
```

Note

The algorithm is slow. On a reasonable desktop machine, the surface reconstruction shown below took about 15 minutes.

## Options¶

- density
- Write an estimate of neighborhood density for each point in the output set.
- depth
- Maximum depth of the tree used for reconstruction. The output is sentsitve
to this parameter. Increase if the results appear unsatisfactory.
[Default:
**8**]