Pointcloud Processing Tooling

28th February 2024

For some of the more recent LIDAR maps I’ve been producing over the past six months - see this post with initial experimentation, and the resulting full-sized maps I’ve rendered so far can be found here - the source data has been in Pointcloud form rather than as a ‘flat’ raster image format (GeoTIFF) of just the DSM height values, and so it requires different processing in order to clean it up, and then convert it into a ‘displacement’ image map so I can render it as a 3D geometry mesh.

The below rendering is of Sydney, and uses a conversion from Pointcloud source data (available from https://elevation.fsdf.org.au/ as .laz files):

The data being in Pointcloud format has both advantages and disadvantages over more simple raster height images: one of the main advantages is that there’s often (depending on the density and distribution of the points) more data per 2D area measure of ground, and each point has separate positions and attributes, rather than just being an average height value for the pixel as it is in raster image format. This means that theoretically it’s easier to “clean up” the data and remove “bad” points without destroying surrounding data. Another advantage is that being fully-3D, it’s possible to view the points from any direction, which is very useful when visualising and when looking for outliers and other “bad” points.

The disadvantages are that the data is generally a lot heavier and takes up more memory when processing it - three 8-byte double/f64 values need to be stored for the XYZ co-ordinates - at least when read from LAS files, as well as additional metadata per point (although there are ways to losslessly compress the memory usage a bit) - in addition to needing different tools to process than with raster images. Newer QGIS versions do now support opening and viewing .LAS/.LAZ Pointclouds (both in 2D and 3D views), although on Linux I’ve found the 3D view quite unstable, and other than being able to select points to view the classification, there’s not much else you can do to process the points, other than some generic processing which uses the PDAL tooling under-the-hood. It also appears QGIS has to convert the .LAS/.LAZ formats to an intermediate format first, which slows down iteration time when using other processing tooling side-by-side.

PDAL is a library for translating and manipulating Pointcloud data (similar to GDAL for raster data / GeoTIFFs, which QGIS uses for a lot of raster and vector operations under-the-hood), and it has quite a few useful features including merging Pointclouds (a lot of the source DSM Pointcloud data is only available as tiles of areas, and so the data needs to be merged to render entire cities, either before converting to a raster displacement map or after), filtering points, rejecting outliers and converting to raster image heightfields.

I have however found its memory usage somewhat ‘excessive’ for some operations, in addition to being slow (despite the fact it’s written in C++). Because of this - and also to learn more about Pointclouds and the file formats - I’ve started to write my own basic Pointcloud processing utility application (in Rust - the las-rs crate allowed out-of-the-box reading and writing of the .LAS/.LAZ Pointcloud formats which was very useful to get started quickly), which despite not really doing anything especially ‘fancy’ for some of the more simple operations like merging .LAS/.LAZ files - it just does a naive loop over all input files, filtering the points within based on configured filters and then accumulating and saving them to the output file - uses a lot less memory than PDAL does, and is quite a bit faster, so I’ve been able to process larger chunks of data with my own tooling and with faster iteration time.

The one area I haven’t tackled yet and am still using PDAL for is conversion to output raster image (GeoTIFF) - which I then use as the displacement map in my renders - however I hope to implement this rasterisation functionality myself at some point.

I am on the lookout for better Pointcloud visualisation software (in particular on Linux - a lot of the commercial software seems to be Windows or Mac only). QGIS’ functionality is adequate but not great, and is fairly lacking in terms of selection, and other open source software I’ve found like CloudCompare seem a bit unstable (at least when compiling from source on Linux), and it’s not clear how well it’d scale to displaying hundreds of millions of points at once.

I have found displaz which is pretty good for displaying very large Pointclouds (it progressively draws them, and seems to store them efficiently in memory), however it has no support for selection or manipulation of points (by design), so I’m still looking for something which caters to that additional need: in particular the selecting of outlier points interactively and culling them.

LIDAR Digital Surface Model Map Renders

20th July 2023

I’ve started experimenting with rendering representations of LIDAR-measured Digital Surface Model map datasets, which in contrast to Digital Elevation Model or Digital Terrain Model datasets - which are more common, and only consist of the raw terrain elevation data - have human-built structures in the height data (i.e. buildings). Previous Map Renderings (A full list of ones I’ve done so far can be found here) have involved DEM model data which just consists of the natural terrain, and in terms of scale, I’ve focused there on rendering entire countries or islands.

So I’ve been curious to try generating more detailed imagery of more localised areas, in particular of cities where human-made buildings and architecture are clearly visible, and here are some very early initial attempts with the raw DSM data for London.

The above render (Full 2.5k Render) is just a plain render of the surface being displaced by the LIDAR dataset values, using data from the UK’s Environment Agency from the LIDAR Composite DSM 2022 - 1m dataset showing central London.

(Note that when downloading the tilesets from the website, the category defaults to the ‘DTM’ Digital Terrain Model version which doesn’t have buildings, so ensure you switch to the ‘DSM’ version if that’s the one you want.)

When zoomed out and viewed from above, the fidelity seems pretty good: showing buildings, bridges and trees in nice detail. Using a slightly less boring shader look - an occlusion shader, driving a diffuse surface colour gradient - gives what I think is a quite pleasing effect, accentuating the streets: (Full 2.5k Render)

The height scale I’ve used is not physically-based to real scale (to the horizontal extents) currently: I’ve just eyeballed something which looks fairly reasonable, but I think it’s probably a bit too high still.

Once you start to look at the resulting generated surface in a bit more detail from closer up or at a more horizontal viewing angle, then the understandable limitations of the fidelity and format (2D single height values) of the data becomes a bit more apparent, especially in situations like overhangs, where single DEM/DSM values per point on a 2D surface are obviously limited. This can be seen in representations of bridges (there’s no gap underneath them), Tower Bridge and the London Eye in the below alternative view render:

London did seem to previously have some 25 cm resolution DSM datasets available at some point, but data.gov.uk doesn’t seem to have those any more, although they may still be part of other overall datasets, so I’ll try and find them. That won’t solve the ‘overhang’ problem (you’d need to use a full 3D point-cloud representation for that), but it should provide extra detail which might be interesting.

I’d also like to try and colour in water areas (and maybe foliage-heavy areas like parks) specific colours to add a bit more contrast and produce some more “artistic” versions, which I’ll look into doing over the next few months. Looking at rendering other cities that have more hilly terrain than London might be interesting as well, in order to have a combination of terrain and human-built buildings. I did look to see if I could find LIDAR DSM models of Wellington, NZ (where I’m currently living), which has an interesting combination of the two (although most buildings are quite small on the hills here), but I could only find DEM terrain models (without structures) for NZ. Cities like San Francisco are likely to have good data in this category, and I’ll have a look at other cities as well to see what’s available.

Tags: map lidar render rendering imagery