Combining Voxel and Normal Predictions for Multi-View 3D Sketching

Johanna Delanoy 1 David Coeurjolly 2 Jacques-Olivier Lachaud 3 Adrien Bousseau 1
1 GRAPHDECO - GRAPHics and DEsign with hEterogeneous COntent
CRISAM - Inria Sophia Antipolis - Méditerranée
2 M2DisCo - Geometry Processing and Constrained Optimization
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
3 LAMA - Laboratoire de Mathématiques
Abstract : Recent works on data-driven sketch-based modeling use either voxel grids or normal/depth maps as geometric representations compatible with convolutional neural networks. While voxel grids can represent complete objects-including parts not visible in the sketches-their memory consumption restricts them to low-resolution predictions. In contrast, a single normal or depth map can capture fine details, but multiple maps from different viewpoints need to be predicted and fused to produce a closed surface. We propose to combine these two representations to address their respective shortcomings in the context of a multi-view sketch-based modeling system. Our method predicts a voxel grid common to all the input sketches, along with one normal map per sketch. We then use the voxel grid as a support for normal map fusion by optimizing its extracted surface such that it is consistent with the re-projected normals, while being as piecewise-smooth as possible overall. We compare our method with a recent voxel prediction system, demonstrating improved recovery of sharp features over a variety of man-made objects.
Keywords : Voxels Normal Map Sketch-Based Modeling Deep Learning Surface Regularization
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Submitted on : Monday, May 27, 2019 - 8:18:03 PM
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Johanna Delanoy, David Coeurjolly, Jacques-Olivier Lachaud, Adrien Bousseau. Combining Voxel and Normal Predictions for Multi-View 3D Sketching. Computers and Graphics, Elsevier, 2019, Proceedings of Shape Modeling International 2019, 82, pp.65--72. ⟨10.1016/j.cag.2019.05.024⟩. ⟨hal-02141469⟩

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