Parameterize Structure With Differentiable Template for 3D Shape Generation

Structural representation is crucial for reconstructing and generating editable 3D shapes with part semantics. Recent 3D shape generation works employ complicated networks and structure definitions relying on hierarchical annotations and pay less attention to the details inside parts. In this paper,...

Description complète

Détails bibliographiques
Publié dans:IEEE transactions on visualization and computer graphics. - 1996. - 31(2025), 10 vom: 29. Sept., Seite 8915-8927
Auteur principal: Ma, Changfeng (Auteur)
Autres auteurs: Guo, Pengxiao, Yang, Shuangyu, Chen, Yinuo, Guo, Jie, Wang, Chongjun, Guo, Yanwen, Wang, Wenping
Format: Article en ligne
Langue:English
Publié: 2025
Accès à la collection:IEEE transactions on visualization and computer graphics
Sujets:Journal Article
LEADER 01000naa a22002652c 4500
001 NLM392021250
003 DE-627
005 20250906233628.0
007 cr uuu---uuuuu
008 250906s2025 xx |||||o 00| ||eng c
024 7 |a 10.1109/TVCG.2025.3583987  |2 doi 
028 5 2 |a pubmed25n1558.xml 
035 |a (DE-627)NLM392021250 
035 |a (NLM)40577299 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Ma, Changfeng  |e verfasserin  |4 aut 
245 1 0 |a Parameterize Structure With Differentiable Template for 3D Shape Generation 
264 1 |c 2025 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Revised 05.09.2025 
500 |a published: Print 
500 |a Citation Status PubMed-not-MEDLINE 
520 |a Structural representation is crucial for reconstructing and generating editable 3D shapes with part semantics. Recent 3D shape generation works employ complicated networks and structure definitions relying on hierarchical annotations and pay less attention to the details inside parts. In this paper, we propose the method that parameterizes the shared structure in the same category using a differentiable template and corresponding fixed-length parameters. Specific parameters are fed into the template to calculate cuboids that indicate a concrete shape. We utilize the boundaries of three-view renderings of each cuboid to further describe the inside details. Shapes are represented with the parameters and three-view details inside cuboids, from which the SDF can be calculated to recover the object. Benefiting from our fixed-length parameters and three-view details, our networks for reconstruction and generation are simple and effective to learn the latent space. Our method can reconstruct or generate diverse shapes with complicated details, and interpolate them smoothly. Extensive evaluations demonstrate the superiority of our method on reconstruction from point cloud, generation, and interpolation 
650 4 |a Journal Article 
700 1 |a Guo, Pengxiao  |e verfasserin  |4 aut 
700 1 |a Yang, Shuangyu  |e verfasserin  |4 aut 
700 1 |a Chen, Yinuo  |e verfasserin  |4 aut 
700 1 |a Guo, Jie  |e verfasserin  |4 aut 
700 1 |a Wang, Chongjun  |e verfasserin  |4 aut 
700 1 |a Guo, Yanwen  |e verfasserin  |4 aut 
700 1 |a Wang, Wenping  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on visualization and computer graphics  |d 1996  |g 31(2025), 10 vom: 29. Sept., Seite 8915-8927  |w (DE-627)NLM098269445  |x 1941-0506  |7 nnas 
773 1 8 |g volume:31  |g year:2025  |g number:10  |g day:29  |g month:09  |g pages:8915-8927 
856 4 0 |u http://dx.doi.org/10.1109/TVCG.2025.3583987  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 31  |j 2025  |e 10  |b 29  |c 09  |h 8915-8927