This is a project on photometric stereo reconstruction. An object is observed by a fixed camera under different illumination. So we have a dense set of images to start with. The challenge is to infer a 2.5D surface description of the object (that is, a depth model), despite that the captured data are severely contaminated by shadows, highlights, transparency and that the light calibration is inaccurate.
Dense Photometric Stereo Using a Mirror Sphere and Graph Cut
The steps of the project are:
1: uniform resampling
2: find denominator image
3: initial normal estimation
4: refine normals by MRF graph cut
5: contruct 3D models
In this part, we include several examples to demonstrate.
example 02
example 03
example 04
example 05
example 06
example 07
example 08
example 09
example 10
