Accurate color reproduction is important in many applications of 3D printing, from design prototypes to 3D color copies or portraits. Although full color is available via other technologies, multi-jet printers have greater potential for graphical 3D printing, in terms of reproducing complex appearance properties. However, to date these printers cannot produce full color, and doing so poses substantial technical challenges, from the shear amount of data to the translucency of the available color materials. In this article, we propose an error diffusion halftoning approach to achieve full color with multi-jet printers, which operates on multiple isosurfaces or layers within the object. We propose a novel traversal algorithm for voxel surfaces, which allows the transfer of existing error diffusion algorithms from 2D printing. The resulting prints faithfully reproduce colors, color gradients and fine-scale details.
Due to the low computational effort and high quality of 2D error diffusion achieved with small diffusion filters, we decided to adapt it to 3D color printing. One prior work addresses error diffusion on a surface @cite_11 . This approach operates on meshes and traverses the vertices based on the availability of subsequent moves or neighbors to diffuse error to. While this approach could be applied to any graph structure, including voxels, it is not clear that it can be applied in a streaming architecture. To the best of our knowledge, we are the first to consider error diffusion halftoning in the context of both non-Euclidean domains and highly translucent materials. Moreover, we are the first to propose such a technique demonstrated to be applicable in practice to tens of billions of elements.
@cite_11
2007290376 2020429267 2142586112 2462462929
We consider the problem of quantization for surface graphs. In particular, we generalize the process of error diffusion to meshes and then compare different paths on the surface when used for error diffusion. We suggest paths for processing mesh elements that lead to better distributions of available neighbors for error diffusion. We demonstrate the potential benefit of error diffusion at several mesh processing applications: quantization of differential mesh coordinates, including an extension to animated geometry, and vertex subset selection for mesh simplification. These applications allow us to compare different paths objectively. We find that the linear time solution results in excellent overall performance, outperforming other traversals taken from the literature. We conclude that the proposed path can be taken as a starting point for any application of error diffusion on meshes. Graphical abstractDisplay Omitted HighlightsThe problem of quantization for surface graphs is considered.The process of error diffusion generalized to meshes.Paths for processing mesh elements are suggested.The benefit of error diffusion at mesh processing applications is demonstrated. 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. This paper proposes a new method for isotropic remeshing of triangulated surface meshes. Given a triangulated surface mesh to be resampled and a user-specified density function defined over it, we first distribute the desired number of samples by generalizing error diffusion, commonly used in image halftoning, to work directly on mesh triangles and feature edges. We then use the resulting sampling as an initial configuration for building a weighted centroidal Voronoi tessellation in a conformal parameter space, where the specified density function is used for weighing. We finally create the mesh by lifting the corresponding constrained Delaunay triangulation from parameter space. A precise control over the sampling is obtained through a flexible design of the density function, the latter being possibly low-pass filtered to obtain a smoother gradation. We demonstrate the versatility of our approach through various remeshing examples. In this paper, we propose a non-local structured prior for volumetric multi-view 3D reconstruction. Towards this goal, we present a novel Markov random field model based on ray potentials in which assumptions about large 3D surface patches such as planarity or Manhattan world constraints can be efficiently encoded as probabilistic priors. We further derive an inference algorithm that reasons jointly about voxels, pixels and image segments, and estimates marginal distributions of appearance, occupancy, depth, normals and planarity. Key to tractable inference is a novel hybrid representation that spans both voxel and pixel space and that integrates non-local information from 2D image segmentations in a principled way. We compare our non-local prior to commonly employed local smoothness assumptions and a variety of state-of-the-art volumetric reconstruction baselines on challenging outdoor scenes with textureless and reflective surfaces. Our experiments indicate that regularizing over larger distances has the potential to resolve ambiguities where local regularizers fail.