24 February 2017 Improving 3D surface reconstruction from endoscopic video via fusion and refined reflectance modeling
Author Affiliations +
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.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rui Wang, Rui Wang, True Price, True Price, Qingyu Zhao, Qingyu Zhao, Jan-Michael Frahm, Jan-Michael Frahm, Julian Rosenman, Julian Rosenman, Stephen Pizer, Stephen Pizer, } "Improving 3D surface reconstruction from endoscopic video via fusion and refined reflectance modeling", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330B (24 February 2017); doi: 10.1117/12.2254587; https://doi.org/10.1117/12.2254587

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