3D range scan enhancement using image-based methods

This paper addresses the problem of 3D surface scan refinement, which is desirable due to noise, outliers, and missing measurements being present in the 3D surfaces obtained with a laser scanner. We present a novel algorithm for the fusion of absolute laser scanner depth profiles and photometrically estimated surface normal data, which yields a noise-reduced and highly detailed depth profile with large scale shape robustness. In contrast to other approaches published in the literature, the presented algorithm (1) regards non-Lambertian surfaces, (2) simultaneously computes surface reflectance (i.e. BRDF) parameters required for 3D reconstruction, (3) models pixelwise incident light and viewing directions, and (4) accounts for interreflections. The algorithm as such relies on the minimization of a three-component error term, which penalizes intensity deviations, integrability deviations, and deviations from the known large-scale surface shape. The solution of the error minimization is obtained iteratively based on a calculus of variations. BRDF parameters are estimated by initially reducing and then iteratively refining the optical resolution, which provides the required robust data basis. The 3D reconstruction of concave surface regions affected by interreflections is improved by compensating global illumination in the image data. The algorithm is evaluated based on eight objects with varying albedos and reflectance behaviors (diffuse, specular, metallic). The qualitative evaluation shows a removal of outliers and a strong reduction of noise, while the large scale shape is preserved. Fine surface details Which are previously not contained in the surface scans, are incorporated through using image data. The algorithm is evaluated with respect to its absolute accuracy using two caliper objects of known shape, and based on synthetically generated data. The beneficial effect of interreflection compensation on the reconstruction accuracy is evaluated quantitatively in a Photometric Stereo framework.

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