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.
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.
@cite_3
2020429267
We present a novel method to obtain fine-scale detail in 3D reconstructions generated with low-budget RGB-D cameras or other commodity scanning devices. As the depth data of these sensors is noisy, truncated signed distance fields are typically used to regularize out the noise, which unfortunately leads to over-smoothed results. In our approach, we leverage RGB data to refine these reconstructions through shading cues, as color input is typically of much higher resolution than the depth data. As a result, we obtain reconstructions with high geometric detail, far beyond the depth resolution of the camera itself. Our core contribution is shading-based refinement directly on the implicit surface representation, which is generated from globally-aligned RGB-D images. We formulate the inverse shading problem on the volumetric distance field, and present a novel objective function which jointly optimizes for fine-scale surface geometry and spatially-varying surface reflectance. In order to enable the efficient reconstruction of sub-millimeter detail, we store and process our surface using a sparse voxel hashing scheme which we augment by introducing a grid hierarchy. A tailored GPU-based Gauss-Newton solver enables us to refine large shape models to previously unseen resolution within only a few seconds.