Passive systems: photogrammetry and other image based methods
This page describes traditional photogrammetry and semi-automated image-based methods Structure-From-Motion (SFM) and Dense Multi-View 3D Reconstruction (DMVR).
Photogrammetry is the primary image-based method used to determine the 2D and 3D geometric properties of objects that are visible in an image set. The method involves calibrating stereo images, feature extraction, feature correspondence analysis and depth computation based on corresponding points. It is a simple and low cost (in terms of equipment) approach; its main challenge involves the task of correctly identifying common points between images.
The determination of the attitude, position and the intrinsic geometric characteristics of the camera are fundamental photogrammetric problems. It can be described as the determination of camera interior and exterior orientation parameters, and the determination of the 3D coordinates of points on the images.
Photogrammetry can be used both on the ground and in the air. In aerial photogrammetry, images are acquired from an aircraft or an UAV, whilst in terrestrial photogrammetry images are captured from near or on the surface of the earth. When the object size and the distance between the camera and object are less than 100m then terrestrial photogrammetry is also defined as close range photogrammetry.
Module 1.2 of the video training course illustrates methods for capturing overlapping images of small and large museum objects including camera setup and lighting.
The accuracy of photogrammetric measurements is largely a function of the camera’s optics quality and sensor resolution. Current commercial and open photogrammetric software solutions are able to quickly perform tasks such as camera calibration, epipolar geometry computations and textured map 3D mesh generation. Common digital images can be used and under suitable conditions high accuracy measurements can be obtained. The method can be considered objective and reliable. Using modern software solutions it can be relatively simple to apply and has a low cost. When combined with accurate measurements derived from a total station for example it can produce models of high accuracy for scales of 1:100 and even higher.
Overlapping area of images captured at A and B are resolved within the 3D model space to enable the precise and accurate measurement of the model.
In recent times, increase in the computation power has allowed the introduction of semi automated image-based methods. An example is the combination of Structure-From-Motion (SFM) and Dense Multi-View 3D Reconstruction (DMVR) methods. A number of software solutions implementing SFM-DMVR algorithms from unordered image collections are available to the broad public.
SFM is considered an extension of stereo vision. Instead of image pairs the method attempts to reconstruct depth from a number of unordered images that depict a static scene or an object from arbitrary viewpoints.
As well as feature extraction phase, the trajectories of corresponding features over the image collection are computed. The method mainly uses corresponding features, shared between different images that depict overlapping areas, to calculate the intrinsic and extrinsic parameters of the camera. These parameters are related to the focal length, the image format, the principal point, the lens distortion coefficients, the location of the projection centre and the image orientation in 3D space. Many systems include adjustment methods in to improve the accuracy of calculating the camera trajectory within the image collection, minimise the projection error and prevent the error-built up of the camera position tracking.
The principles of structure from motion (SFM) measurement from multiple overlapping images.