Abstract
Shape-from-shading and stereo vision are two complementary methods to reconstruct 3D surface from images. Stereo vision can reconstruct the overall shape well but is vulnerable in texture-less and non-Lambertian areas where shape-from-shading can recover fine details. This paper presents a novel, generic shading based method to refine the surface generated by multi-view stereo. Different from most of the shading based surface refinement methods, the new development does not assume the ideal Lambertian reflectance, known illumination, or uniform surface albedo. Instead, specular reflectance is taken into account while the illumination can be arbitrary and the albedo can be non-uniform. Surface refinement is achieved by solving an objective function where the imaging process is modeled with spherical harmonics illumination and specular reflectance. Our experiments are carried out using images of indoor scenes with obvious specular reflection and of outdoor scenes with a mixture of Lambertian and specular reflections. Comparing to surfaces created by current multi-view stereo and shape-from-shading methods, the developed method can recover more fine details with lower omission rates (6.11% vs. 24.25%) in the scenes evaluated. The benefit is more apparent when the images are taken with low-cost, off-the-shelf cameras. It is therefore recommended that a general shading model consisting of varying albedo and specularity shall be used in routine surface reconstruction practice.
Highlights
Reconstruction of 3D surface from multi-view images is of great interest in recent decades
We have proposed a shading-based surface refinement surface refinement under varying albedo and specularity (SREVAS) method, which can be used for reconstructing surfaces with varying albedo and specular reflection
Starting from this imaging model, we use an objective function to refine the initial surface generated by multi-view stereo (MVS)
Summary
Reconstruction of 3D surface from multi-view images is of great interest in recent decades. The combination of structure from motion (SfM) [1,2,3] and multi-view stereo (MVS) [4,5,6,7] can reconstruct the 3D shape of a scene with multiple images. SfM helps to estimate the camera parameters including interior orientation parameters (focal length, principal point position and lens distortion parameters) and exterior orientation parameters (camera locations and orientations), while MVS attempts to reconstruct the 3D shape by searching corresponding pixels or other features from images. According to [8], the major challenges of the current MVS algorithms are texture-poor objects, thin structures, and non-Lambertian surfaces. It makes MVS reconstruction harder when the images are captured with varying illumination conditions. Reliable and accurate correspondences are difficult to establish and fine details cannot be well restored
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