Site Prediction |
Vision based technique for photorealistic 3D reconstruction of historical items
The system uses non-metric CCD camera equipped with 12-mm lens as image acquisition device. To provide metric characteristics of reconstructed objects system is preliminary calibrated by special procedure based on test field. The plane containing 49 reference points (Fig. 2.) is used as test field for calibration procedure which includes the following steps:
The basic method for unknowns determination during calibration process is least mean squares (LMS) estimation. For automating most time consuming procedures as image coordinate measurements and choosing initial values for unknowns original coded targets and automate initial approximation determination algorithm are used (Knyaz, 1998).
- Cameras interior orientation
- Cameras exterior orientation
- Stripe structural light projector calibration
- Turntable axis of rotation estimation
Figure 2. Test field for calibration
The coded targets used as reference points provide invariance to rotation and scaling, robust recognition of a target number with correction of possible mistakes, exact measurement of a target center, target detection in the image with non-uniform contrast, short processing time. Elliptic approximation is used for high accuracy sub-pixel center of target determination.
3D Reconstruction Technique
- Interior orientation
Original calibration procedure (Knyaz, 1999) for low precision test field is used. It uses the next form of additional parameters describing CCD camera model (Beyer,1992) in collinearity conditions:
and allows to determine the parameters of interior orientation (principal point xp, yp, scales in x and y directions mx, my, affinity factor a, the radial symmetric K1,K2,K3 distortion and decentering P1,P2 distortion) using reference distances between several reference points measured with high precision and condition of plane test field.
The exterior orientation parameters ((Xi, Yi, Zi) - location and
(ai,wi,ki) angle position of the left, right and upper (i=1,2,3) camera) are determined in external coordinate system connected with turntable. The same plane test field (Fig. 2.) is used. To resolve the problem of unreliable exterior orientation based on plane test field three stereo pair of test field situated at the three various locations within workspace are acquired. At LMS estimating stage all reference points are treated as united spatial test field consisting of three planes.
Light plane determination
For determination of spatial coordinates for object points viewed only by one camera the following suggestion is used: the stripe line on the object produced by structured light projector is the line of intersection of the some plane with reconstructed object. For accurate determination of S plane orientation in exterior coordinate system a set of stereo images of test field is acquired, test field being located at several positions in workspace. Then stripe is extracted in the images and its 3D coordinates are calculated. The set of spatial lines determines the plane of structured light in exterior coordinate system. The plane parameters (a,b,c,d) are estimated by least mean square estimation.
- Turntable rotation axis determination
Another suggestion used for 3D reconstruction is the following: contrast line found in the image is the intersection of light plane with object which rotates with given step around given axis. The estimation of rotation axis is based on image acquisition of plane test field in n several positions while rotating the turntable. Then spatial coordinates of reference points are calculating basing on known exterior orientation of cameras for each angle position
ji, i = 1,…,n of test field. The vector of unknown parameters for least mean squares estimation includes rotation axis orientation
(Xa, Ya, Za ,
wa, ka) and angles of rotation
ji, i = 1,…,n.
The samples of 3D models (American Indian vase and skull) produced by described system are shown in Fig. 3. The following technique is used for 3D reconstruction. To determine spatial coordinate of object point presenting in the image this point has to be identified as satisfied to some geometrical condition. For point identification structural light projector is used. Stripe projecting allows simple recognizing points of intersection light plane and the object. Then all lighted points viewed at least by one camera are included in 3D model.
Figure 3. Results of 3D reconstruction for complex objects
For spatial coordinate determination two methods are used. For lighted object point presenting at least in one image the condition of intersection light plane S and object is used for 3D coordinate calculating. If lighted object point is viewed by two cameras then correspondence problem is solved and 3D coordinate of the point is calculated from collinearity condition.
For 3D-model reconstruction the procedures of image acquisition in structured light and image processing are performed. These procedures are executed in cycle while given angle position of turntable is reached. On each step stripe is detected with sub-pixel accuracy by weight operator and image coordinates of stripe are recalculated to undistorted values basing on interior orientation results. The results of stripe detecting for each table angle are stored in a file.
Spatial coordinates are calculated according two described methods resulting in "cloud" of 3D coordinates. To resolve the problem of reconstructing the surface for object of complex spatial topology the method of projection on spatial figure like sphere or cylinder is used (Knyaz, 2000). Thus technique allows obtaining single valued projection, so the topology of object can be reconstructed by Delaunay triangulation procedure for projected points. Then the links between projected points are used for generation spatial surface of the object.