If you are using this dataset, please cite: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8206372
@inproceedings{abelha2017learning,
title={Learning How a Tool Affords by Simulating 3D Models from the Web},
author={Abelha Ferreira, Paulo and Guerin, Frank},
booktitle={Proceedings of IEEE International Conference on Intelligent Robots and Systems (IROS 2017)},
year={2017},
organization={IEEE Press}
}
ToolWeb dataset of 116 point clouds of synthetic household tools with mass and affordance groundtruth for 5 tasks. By Paulo Abelha
For this dataset we gathered 116 CAD models from 3DWarehouse and re-sampeld an re-meshed them with an automatic Meshlab script. Additionally, all meshes were segmented using my code for batch segmentation (https://github.com/pauloabelha/batch_segmentation)
This dataset containts the final re-sampeld/re-meshed and segmented point clouds together with mass and affordance groundtruth for 5 tasks.