Although the demand for and the degree of precision in modeling may depend on the contexts/applications, dimensional precision influences the perception and conception of the viewer. Additionally, as part of the essential properties of object/space representation, dimension is one of the key parameters required in the modeling process and by the aforementioned specialised software.
As vision is vital to human activities, plannar images are commonly available in our environment as a source of information and/or as a form of record mainly for information purposes. Thus, in addition to the direct measurements for the subject of interest, dimensional data can also be acquired from the plannar images (in particular photographs) of the subject of interest via parametric measurements.
In this presentation, a technique of dimensional estimation is proposed based on the mathematical concept of geometry.
Step 1: The Volume Ratio and Scale Factors
In a plannar image (e.g. photograph), although the overall size and rotations of the subject of interest may differ, the contour of such subject is preserved. Thus, in an ideal condition that the overall rotations (facing the viewer) of the subject of interest in reality is the same as the one in the plannar image (with no extra changes but overall size), the dimensions measured on the plannar image (e.g. photograph) for the subject of interest may be used for estimating the dimension of the subject of interest in reality by implementing the mathematical concept of volume ratio or scale factors.
(the following video is credited to the YouTube user: Scott Davidson)
Step 2: Case Study 1
Step 3: Collection of Metric Data and Measurements
Regarding the technique/tool of measurement, a ruler is used in this case as an example. For more precise measurements, a digital way is preferred, e.g. by/in software like Adobe Photoshop.
Step 4: Considerations of Different View Angles/perspectives
Thus, it is neccessary to take into account additional factors that may influence the calculations. The most prominent factors would be the distance between the camera and the object, the rotation of the object and the field of view (FOV) of the camera.
After a virtual experiment conducted in 3DS MAX using simple meshes, data are collected and tabulated as table 2 while the significance of the factor is estimated by one way ANOVA statistical analysis (95% confidence). It has been shown that there are no significant differences in the angle θ when the condition is varied by FOV only while both the camera distances and object rotations alone created significant differences.
As a result, the optimized calculations will involve:
1) the camera distances to the object; and
2) the rotation angles of the object relative to the camera view point
Step 5: The Optimized Algorithm
Step 6: Data Analysis
Step 7: (Optional) Implementation of the Data
Step 8: Summary
1) gather dimensional parametric data of the subject of interest from different photos (in different perspectives) by using ratios/scale factors.
2) estimate the dimension of the subject of interest via photos.
3) create a simple 3D model with basic materials and textures.
P.S: this presentation is not completed yet but publish for testing purpose only.
1) University of Hong Kong
2) The Hong Kong Polytechnic University
1) Brightstorm | Volume Ratios (http://www.brightstorm.com/math/geometry/similarity/similarity-and-volume-ratios/)
2) Scott Davidson | Sec 12.7: Similar Solids (http://www.youtube.com/embed/vtTDLHKnxOo?rel=0)