Marko Vrabec University of Ljubljana, Department of Geology

Multiview Photogrammetry – 3D Virtual Geology for everyone

A short course

FIRST: some background info

Precarious structural measurements of fractures in Quaternary slope breccia, Idrija fault zone, Julian Alps

SECOND: course outline

1. A (very) brief introduction to photogrammetry 2. Image acquisition techniques – the full rundown

coffee break & practical image acquisition

3. Hands-on introduction to the modeling workflow with Agisoft Photoscan software coffee break

4. Special topics: geopositioning of models, hardware considerations, exporting models,... 5. A few selected examples

POST-WORKSHOP BARBECUE

When recording field observations... • How successful are we? • How efficient are we? • How (un)biased are we? • How repeateable are our observations?

HOW DO WE RECORD AND VISUALIZE FIELD INFORMATION?

When recording field observations... • How successful are we? • How efficient are we? • How (un)biased are we? • How repeateable are our observations? 3D virtual geology to the rescue!

HOW DO WE RECORD AND VISUALIZE FIELD INFORMATION?

What is the amount of capital and expertise required to enter the virtual 3D world?

3D VIRTUAL GEOLOGY – ROCKET SCIENCE OR...?

What is the amount of capital and expertise required to enter the virtual 3D world? Democratisation of access to 3D data acquisition and analysis techniques!

3D VIRTUAL GEOLOGY – ROCKET SCIENCE OR...?

„...the art, science, and technology of obtaining precise mathematical measurements and three-dimensional (3D) data from two or more photographs“ (Matthews 2008). The main component necessary for a photogrammetric project is a series of overlapping stereoscopic* images. (*i.e., images showing the same features from varying point(s) of view)

PHOTOGRAMMETRY IS...

STEREOPHOTOGRAMMETRY – everybody is doing it

STEREOPHOTOGRAMMETRY – everybody is doing it

• with (relatively) simple triangulation it is possible to calculate 3D positions (x,y,z) of object points from stereo pairs

• HOWEVER, we need to know either: • camera 3D location and pose (yaw, pitch, roll) • or 3D coordinates of a series of ground control points (GCPs) appearing in the

scene • PLUS we want to have as undistorted and metrically correct source images as possible

• providing this is complicated and expensive

METRIC STEREOPHOTOGRAMMETRY – NOT everybody is doing it

Traditional equipment requirements: • specially configured and calibrated metric cameras (frightfuly expensive) • photogrammetric workstation and software plus extensive training and experience

METRIC STEREOPHOTOGRAMMETRY – NOT everybody is doing it

• derived from 1990‘s research in computer vision applications • a large number of closely spaced, overlapping images („Multi-view photogrammetry“) • camera pose and scene geometry are solved simultaneously • results in a 3D point cloud in relative image space (which can be georeferenced later) • requires no special equipment, no calibration, no targets, a fully automated procedure • a true, revolutionary democratisation of photogrammetry (here we go again....)

STRUCTURE-FROM-MOTION (SfM) ALGORITHM

1) IMAGE ACQUISITION • systematically cover your scene with a dense set of highy-overlapping photos (min. 60%

overlap), taken from different locations • in order to be reconstructed, a single feature must be visible in at least 3 photographs (but

the more, the better)

STRUCTURE-FROM-MOTION (SfM) ALGORITHM – HOW DOES IT WORK?

2) KEYPOINT EXTRACTION • software identifies identical points on different photographs (independent of scale,

orientation, illumination, etc.) • sensitive to image resolution, sharpness, density • also sensitive to object properties and external conditions: material, texture, lighting

STRUCTURE-FROM-MOTION (SfM) ALGORITHM – HOW DOES IT WORK?

3) CAMERA POSE ESTIMATION AND SPARSE CLOUD EXTRACTION • fully automated „sparse bundle adjustment“ algorithm • determination of 3D camera positions and image orientations • low-density point cloud extraction

STRUCTURE-FROM-MOTION (SfM) ALGORITHM – HOW DOES IT WORK?

3) CAMERA POSE ESTIMATION AND SPARSE CLOUD EXTRACTION • fully automated „sparse bundle adjustment“ algorithm • determination of 3D camera positions and image orientations • low-density point cloud extraction

STRUCTURE-FROM-MOTION (SfM) ALGORITHM – HOW DOES IT WORK?

4) DENSE POINT CLOUD EXTRACTION • using information from the previous processing step, a dense point cloud is extracted from

image data

STRUCTURE-FROM-MOTION (SfM) ALGORITHM – HOW DOES IT WORK?

5) SURFACE MODEL CREATION • a continuous surface model is generated by triangulation from dense point cloud

STRUCTURE-FROM-MOTION (SfM) ALGORITHM – HOW DOES IT WORK?

6) SURFACE TEXTURE CREATION • a photographic texture is generated and projected onto the surface model

STRUCTURE-FROM-MOTION (SfM) ALGORITHM – HOW DOES IT WORK?