Reference : PVDeConv: Point-Voxel Deconvolution for Autoencoding CAD Construction in 3D
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/43746
PVDeConv: Point-Voxel Deconvolution for Autoencoding CAD Construction in 3D
English
Cherenkova, Kseniya mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Aouada, Djamila mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Gusev, Gleb mailto []
Oct-2020
5
Yes
IEEE International Conference on Image Processing
25-28 Oct 2020
virtual
virtual
[en] CC3D ; Scan2CAD ; point cloud autoencoding
[en] We propose a Point-Voxel DeConvolution (PVDeConv) mod- ule for 3D data autoencoder. To demonstrate its efficiency we learn to synthesize high-resolution point clouds of 10k points that densely describe the underlying geometry of Computer Aided Design (CAD) models. Scanning artifacts, such as pro- trusions, missing parts, smoothed edges and holes, inevitably appear in real 3D scans of fabricated CAD objects. Learning the original CAD model construction from a 3D scan requires a ground truth to be available together with the corresponding 3D scan of an object. To solve the gap, we introduce a new dedicated dataset, the CC3D, containing 50k+ pairs of CAD models and their corresponding 3D meshes. This dataset is used to learn a convolutional autoencoder for point clouds sampled from the pairs of 3D scans - CAD models. The chal- lenges of this new dataset are demonstrated in comparison with other generative point cloud sampling models trained on ShapeNet. The CC3D autoencoder is efficient with respect to memory consumption and training time as compared to state- of-the-art models for 3D data generation.
Artec3D
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/43746

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