From Intensity Profile to Surface Normal: Photometric Stereo for Unknown Light Sources and Isotropic Reflectances

Abstract

We propose an uncalibrated photometric stereo method that works with general and unknown isotropic reflectances. Our method uses a pixel intensity profile, which is a sequence of radiance intensities recorded at a pixel under unknown varying directional illumination. We show that for general isotropic materials and uniformly distributed light directions, the geodesic distance between intensity profiles is linearly related to the angular difference of their corresponding surface normals, and that the intensity distribution of the intensity profile reveals reflectance properties. Based on these observations, we develop two methods for surface normal estimation; one for a general setting that uses only the recorded intensity profiles, the other for the case where a BRDF database is available while the exact BRDF of the target scene is still unknown. Quantitative and qualitative evaluations are conducted using both synthetic and real-world scenes, which show the state-of-the-art accuracy of smaller than 10 degree without using reference data and 5 degree with reference data for all 100 materials in MERL database.

Finger-position optimization by using caging qualities

“…Experimental results demonstrated the robustness and efficiency of our proposal. In the future, we plan to combine our approach with real object models recovered from vision systems [41,42] and implement it on real robots.…”

Photometric Stereo with Twin-Fisheye Cameras

“…local and non-strictly directional lighting) [14][15][16], more sophisticated surface reflectance models (i.e. "Bidirectional Reflectance Distribution Functions" or BRDF, other than the classical Lambertian reflectance) [17][18][19][20], the relaxation of constraints on camera placement [21][22][23] and combinations thereof [24]. Alongside these recent developments, Woodham's original formulation has been also extended from orthographic to perspective projection in [25].…”

Separate in Latent Space: Unsupervised Single Image Layer Separation

“…Bi et al (Bi, Han, and Yu 2015) applied sparse prior to tackle image intrinsic decomposition. However, the above mentioned methods can only handle simple cases with image gradient or color changes, and can not be adopted to more complex situations that may require other techniques such as photometric stereo (Lu et al 2015;.…”