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Journal articles
Combining Voxel and Normal Predictions for Multi-View 3D Sketching
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.
Cited literature [23 references]
https://hal.archives-ouvertes.fr/hal-02141469
Contributor : David Coeurjolly <>
Submitted on : Monday, May 27, 2019 - 8:18:03 PM
Last modification on : Friday, November 6, 2020 - 3:25:19 AM
Contributor : David Coeurjolly <>
Submitted on : Monday, May 27, 2019 - 8:18:03 PM
Last modification on : Friday, November 6, 2020 - 3:25:19 AM
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paper-RR.pdf
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