> For the complete documentation index, see [llms.txt](https://ms-kb.msd.unimelb.edu.au/next-lab/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ms-kb.msd.unimelb.edu.au/next-lab/3d-scanning/guides/multimodal-digitisation/lidar-x-photogrammetry.md). # LiDAR x Photogrammetry Combining LiDAR and photogrammetry leverages the strengths of both: LiDAR provides highly accurate 3D measurements and structural data to scaffold the photogrammetry model. The photogrammetry then adds detailed color and texture from photos. The model calculated from both datasets can also be stronger.

Example 1

something

Example 2

## How Does it work?

### LiDAR (TLS/SLAM) Start here:


BLK360 Terrestrial LiDAR Scanner
VLX LiDAR SLAM Scanner

LiDAR (Light Detection and Ranging) uses laser pulses from a scanner to measure distances to surfaces. It captures precise 3D information about the shape and features of the terrain and objects by measuring the time it takes for the laser pulses to return. This allows for highly accurate mapping of the environment’s structure and details in three dimensions. However, LiDAR does not capture good color or texture information, meaning the resulting data lacks the visual detail that photos provide. It is best to completely process and clean the LiDAR dataset first. ### Photogrammetry Start here: {% content-ref url="/pages/-M20qXkNtyBvaCgIVA4g" %} [Photogrammetry](/next-lab/3d-scanning/guides/photogrammetry.md) {% endcontent-ref %} Photogrammetry uses overlapping photos taken from the camera to create 3D models. It works by analysing the images to find common points and reconstruct the shape and position of objects. It provides rich detail and texture to the object. However, Photogrammetry has no inherent sense of scale - and while it can result in similar levels of quality, it is highly technique dependent. Our supported software pathways: * **RealityScan** is built for multi-modal digitisation between LiDAR and photogrammetry - its algorithms properly leverage both datasets and can work with both. * **Metashape** is more photogrammetry forward, it expects TLS to be the 'ground-truth' - less flexible. *** ## Combining Datasets Like with all 3d scanning, the workflow relies on **Shared features** between them any dataset. Ensure that there is adequate overlap, ideally, complete overlap between the datasets. {% hint style="warning" %} This is not a technical guide - it will just cover concepts and nuances. You generally use the software as you would usually so refer to the main RealityScan or Metashape guides. We generally recommend **RealityScan** for this process though, so we will use it as the example. {% endhint %} ### Import LiDAR dataset {% hint style="danger" %} **Generally, you must import the LiDAR first.** {% endhint %} In RealityScan, LiDAR datasets can be interpreted in two ways: **Terrestrial LiDAR**\ If using a terrestrial scanner (like our BLK360 or Z+F), this will take the scan positions and convert each image into a cube of photos.

**Or Mobile LiDAR**\ For any point cloud data, Mobile will generate many more scan positions - usually you will adjust in the Virtual Camera Settings in the Import Dialogue box > Virtual Camera Settings: 1. Use Camera Poses: Generate Aerial Poses 2. Pick a height reference, generally local lowest is preferred. 3. Camera cluster should match how the data was captured/produced i.e. drone produced datasets might be best with single camera (from above). 4. Adjust camera height and overlap % to vary the amount of cameras generated.

Mobile LiDAR is preferred as it gives you more reference points for later steps.\ This is also the advantage of RealityScan > it treats everything as part of its workflow (as cameras) so it al works neatly together. {% hint style="info" %} Once imported, a fixed component will be produced, you may disable this until you are ready to use it later. {% endhint %} ### Import Photogrammetry Dataset Import photogrammetry and align it first to get a sense of what you are working with.\ We recommend disabling the LiDAR components first to just focus on photogrammetry.

### Tie Together Using Control Points You may try aligning it all together, if that doesn't work, use Control Points to scaffold the merging + aligning. You will want to inspect each component to see where best to place these Control Points. **Each control point needs to be seen by at least two inputs (per component).**\ **Minimum 3 control points- 4-5 is preferred. These control points cannot be in a single line**. {% hint style="info" %} Name your control points for easier reference. {% endhint %}

Photogrammetry component with multiple correlated control points.

LiDARcomponent with multiple correlated control points.

*** ### Align and Process Align the dataset together and process as you would normally.\ If you wish to only use certain photos or LiDAR for the colouring/texturing, you may change their input weighting (or disable completely) in their 1Ds properties.

*** ## Post-Processing Captured Data

*** ## **Video Guide** This video will guide you through aligning photo datasets with LiDAR scan data if you prefer,\ however some other nuances are covered in the guide above though. {% embed url="" %}