VOX2Surf: Faithful surface extraction from coarse binary voxels

Coarse binary voxel grids (under (Formula presented)) provide a simple interface enabling non-expert users to create a coarse approximation of diverse geometric content. Converting voxelized content into piecewise-smooth geometric models that reflect user intent can greatly increase the attractiveness of such interfaces. While multiple methods exist for surfacing binary voxel grids, they by and large target much higher grid resolutions. Applying these to coarse inputs often produces unintuitive results. We introduce VOX2Surf, a novel method for reconstructing user-intended surfaces from coarse binary voxel grids. We observe that a key challenge in achieving this goal is to correctly identify viewer-expected sharp features in these inputs. While human observers easily mentally separate sharp grid edges that are an artefact of the voxel representation from those depicting intended sharp features, existing techniques struggle to distinguish between them. We employ a learning-based approach, targeted at coarse data, to accurately recover the intended sharp features and utilize them for piecewise-smooth surface fitting. After identifying voxels containing sharp features, we employ a novel geometric reconstruction method to extract a curve network from these voxels. We use the loops of this network as the boundaries of our surface patches and use physically based simulation to smooth both the network curves and the surface patches. Extensive comparisons demonstrate that VOX2Surf achieves better approximation of the input voxelized surfaces compared to alternatives. More importantly, our user study confirms that our results are visually significantly better aligned with viewer expectations when presented with the input surfaces than those produced by alternative approaches.