text: This paper builds upon the current methods to increase their capability and automation for 3D surface construction from noisy and potentially sparse point clouds. It presents an analysis of an artificial neural network surface regression and mapping method, describing caveats, improvements and justification for the different approach.
  @cite_3: Shape-from-shading (SFS) methods tend to rely on models with few parameters because these parameters need to be hand-tuned. This limits the number of different cues that the SFS problem can exploit. In this paper, we show how machine learning can be applied to an SFS model with a large number of parameters. Our system learns a set of weighting parameters that use the intensity of each pixel in the image to gauge the importance of that pixel in the shape reconstruction process. We show empirically that this leads to a significant increase in the accuracy of the recovered surfaces. Our learning approach is novel in that the parameters are optimized with respect to actual surface output by the system. In the first, offline phase, a hemisphere is rendered using a known illumination direction. The isophotes in the resulting reflectance map are then modelled using Gaussian mixtures to obtain a parametric representation of the isophotes. This Gaussian parameterization is then used in the second phase to learn intensity-based weights using a database of 3D shapes. The weights can also be optimized for a particular input image.
summary: In a slightly different problem, where a NN is used to reconstruct the shape of a 3D object from its shading in a 2D @cite_3 . show from experiment that quantitative improvement does not necessarily lead to quantitative improvement. This is something to consider when using a 'black box' function like a neural network, especially where there could be some information loss. In this regard we must ensure that the final model is representative of the ground truth and not only rely on an error measure. It is suggested that more research must be done for 3D surface quality metrics @cite_3 . Visual quality will be assessed in the method presented here alongside quantitative results in the absence of quality metrics.