@article{
 title = {Generalized autoencoder for volumetric shape generation},
 type = {article},
 year = {2020},
 pages = {1082-1088},
 volume = {2020-June},
 id = {f8cfcf29-8e35-3bd2-b98d-a26cb1eeaa16},
 created = {2021-10-25T06:33:57.684Z},
 file_attached = {true},
 profile_id = {235249c2-3ed4-314a-b309-b1ea0330f5d9},
 group_id = {1ff583c0-be37-34fa-9c04-73c69437d354},
 last_modified = {2022-03-28T09:45:08.314Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Guan2020},
 folder_uuids = {a6db5ca6-7f95-48a4-bc40-9e41eea78434,465973dc-f47a-4093-b987-b885254a351b},
 private_publication = {false},
 abstract = {We introduce a 3D generative shape model based on the generalized autoencoder (GAE). GAEs learn a manifold latent space from data relations explicitly provided during training. In our work, we train a GAE for volumetric shape generation from data similarities derived from the Chamfer distance, and with a loss function which is the combination of the traditional autoencoder loss and the GAE loss. We show that this shape model is able to learn more meaningful structures for the latent manifolds of different categories of shapes, and provides better interpolations between shapes when compared to previous approaches such as autoencoders and variational autoencoders.},
 bibtype = {article},
 author = {Guan, Yanran and Jahan, Tansin and Van Kaick, Oliver},
 doi = {10.1109/CVPRW50498.2020.00142},
 journal = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}
}

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