BibTeX
@inproceedings{duret:hal-05220360,
TITLE = {{Breaking the 3D Dataset Bottleneck: Fast Scalable Generation of Aligned 3D Assets from Scratch for Category 6D Pose Estimation and Robotic Grasping}},
AUTHOR = {Duret, Guillaume and Mazurak, Danylo and Zara, Florence and Peters, Jan and Chen, Liming},
URL = {https://hal.science/hal-05220360},
BOOKTITLE = {{IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)}},
ADDRESS = {Denver, United States},
YEAR = {2026},
MONTH = Jun,
KEYWORDS = {3D generation ; Sim2Real ; Dataset and benchmarks ; Category 6D pose estimation ; Grasping},
PDF = {https://hal.science/hal-05220360v3/file/CVPR_2025__Copy_from_Neurips_2025_-18.pdf},
HAL_ID = {hal-05220360},
HAL_VERSION = {v3},
}
@inproceedings{duret:hal-04995939,
TITLE = {{Facilitate and scale up the creation of 3D meshes, 6D category-based datasets and grasping with generative models: genvegefruits3d}},
AUTHOR = {Duret, Guillaume and Bourennane, Younes and Mazurak, Danylo and Samsonenko, Anna and Zara, Florence and Peters, Jan and Chen, Liming},
URL = {https://hal.science/hal-04995939},
BOOKTITLE = {{IEEE 2025 International Conference on Image Processing (ICIP 2025)}},
ADDRESS = {Anchorage, AL, United States},
YEAR = {2025},
MONTH = Sep,
KEYWORDS = {3D generation ; 3D understanding ; Category 6D pose estimation ; Dataset and benchmarks ; Grasping},
PDF = {https://hal.science/hal-04995939v2/file/ICIP2025-12.pdf},
HAL_ID = {hal-04995939},
HAL_VERSION = {v2},
}