Shape from shading (SFS) has been studied for decades; nevertheless, its overly simple assumptions and its ill-conditioning
have resulted in infrequent use in real applications. Price et al. recently developed an iterative scheme named shape from
motion and shading (SFMS) that models both shape and reflectance of an unknown surface simultaneously. SFMS
produces a fairly accurate, dense 3D reconstruction from each frame of a pharyngeal endoscopic video, albeit with
inconsistency between the 3D reconstructions of different frames. We present a comprehensive study of the SFMS scheme
and several improvements to it: (1) We integrate a deformable registration method into the iterative scheme and use the
fusion of multiple surfaces as a reference surface to guide the next iteration’s reconstruction. This can be interpreted as
incorporating regularity of a frame’s reconstruction with that of temporally nearby frames. (2) We show that the reflectance
model estimation is crucial and very sensitive to noise in the data. Moreover, even when the surface reflection is not
assumed to be Lambertian, the reflectance model estimation function in SFMS is still overly simple for endoscopy of
human tissue. By removing outlier pixels, by preventing unrealistic BRDF estimation, and by reducing the falloff speed
of illumination in SFS to account for the effect of multiple bouncing of the light, we improve the reconstruction accuracy.
Rui Wang, True Price, Qingyu Zhao, Jan-Michael Frahm, Julian Rosenman, and Stephen Pizer, "Improving 3D surface reconstruction from endoscopic video via fusion and refined reflectance modeling," Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330B (Presented at SPIE Medical Imaging: February 12, 2017; Published: 24 February 2017); https://doi.org/10.1117/12.2254587.
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