Agisoft Metashape basic workflow

Even if both criteria open source and free software are missing one have to admit that Agisoft’s Metashape is a good tool for deriving point clouds and all kind of surface models from plain RGB imagery. It comes along with an straightforward GUI and in Question of hardware requests and cost efficiency it is well balanced. For a deeper insight one should know how to deal with a lot o settings.

Add the photos to Metashape

Let’s start to follow the white rabbit.

After loading the images into Agisofts Metashape the aerial images are lining up along the flight track as you can see below.

Alignment

The Align Images process is somehow exploitative. For a quick start it is a good practice to use the following settings:

It will give you an good estimate how the general alignment process performs.

However there are a lot of different requirements depending on sensor, light and surface characteristics. For high quality images there is obviously no significant quality improvement if increasing the number of keypoints behind 240000. You may calculate as a rule of thumb (same post) the maximum number of keypoint according to megapixels x 6400.

For the GoPro, Mapir or DJI images this will results in roughly 77000 keypoints. Nevertheless setting the params to zero will be interpreted as maximum which seems to be reliable if one is filtering the point clouds and 3D models in a second step. So a reasonable highest quality approach may look like:

Creating high quality sparse (tie) point clouds

The trick to derive a high quality sparse point clouds is to avoid spurious information and to retrieve a good spatial positioning of the images. In other words you need a optimal alignment with as less as possible “spurious” tie points. You can reach this goals in most cases with a highest quality alignment and posterior filtering and camera optimization process. If still the result is not satisfying your needs, you additionally may also use ground control points GCPs.

We made fairly good experiences following juhutan who suggest the this workflow for generating high quality point clouds. Usually we need only one recursion of gradual selection:

1. Remove sparse points by gradual selection (see below)
2. Optimize cameras (after** each** removal)
3. Optionally repeat steps 1-2 until the reconstruction error is minimized

Ad 1: this removes spurious points from the point cloud e.g. it will smooth the surface

Ad 2: all cameras will be re-optimized for the new geometry

Gradual Selection Process

Before starting the gradual selection process right-click your current chunk in the Workspace window and select “Duplicate chunk”, then proceed with the copy.

1. Choose Reconstruction uncertainty. Type something like 10 and see the as a result all points in the sparse cloud selected Clicking OK and “DEL” will delete the selected points. If the DEL key doesn’t work, you need to click once into the model window to activate it, and press DEL again.
2. Next choose the Reprojection Error option. Set it to < 1 and again delete the selected points then click that wizard’s wand icon again.
3. Finally check the Projection Accuracy try to delete the poorest 10 % of the points.

For more details have a look at e.g. dinosaurpaleo tutorial .

Build 3D model (mesh)

Now you may use the derived sparse point cloud to build a 3D model. the process is straightforward. For an maximum number of faces in the mesh choose the following settings:

Generate Orthophoto

Creating an orthoimage is a very straightforward approach and it differs slightly from deriving a high density point cloud. Just follow the below outlined Workflow.

1. load the prepared images
2. check image quality
3. align images according as proposed before
4. build mesh on an optimized sparse point cloud
5. create orthoimage on the mesh

Using a mesh (3D model) will give in most cases better results for the orthoimage generation of low AGL flight level altitude imagery. Nevertheless you have to check what kind of cloud is more sufficient the dense or sparse point cloud. It depends on the structure. You will get often more satisfactory results using a well preprocessed sparse point cloud.

So the recommended settings are:

Et voila - Finally you derive an orthoimage of fairly high quality

Creating high quality dense point clouds

Hence micro-remote sensing is usually restricted to a single (visible) layer the temporal scale this layer can be observed by drones is the advantage compared to high-cost and temporal limited LIDAR data sets. Using Metashape the dense point cloud might still be classified in different layers depending of the structure of the observed area and structure (i.e a deciduous forest can be observed in winter for ground models and tree structure while summer flights will deliver good surface models).

Additionally dense clouds can be exported in several formats to be used in further processing in QGIS, SAGa or 3D modelbuilder software. So it is easy to benefit from already existing and established workflows which were developed for LIDAR oder 3D modeling data.

The settings of the dense point cloud calculation determines quality and necessary calculation time. Hereby a higher quality results in longer calculation time. Aggressive filtering ignores small structures and runs fastest while moderate and mild filtering consider smaller structures on the cost of longer calculation time.

The dense point cloud can be calculated as soon the pictures are aligned. Even if the dense cloud comes second in the workflow dropdown menu in Metashape it should be considered to calculate the orthoimage first as the dense point cloud will take the longest time of all processes.