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GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency
Dongyue Lu
Lingdong Kong
Tianxin Huang
Gim Hee Lee
National University of Singapore
About 🛠️
GEAL is a novel framework designed to enhance the generalization and robustness of 3D affordance learning by leveraging pre-trained 2D models.
To facilitate robust 3D affordance learning across diverse real-world scenarios, we establish two 3D affordance robustness benchmarks: PIAD-C and LASO-C, based on the test sets of the commonly used datasets PIAD and LASO. We apply seven types of corruptions:
- Add Global
- Add Local
- Drop Global
- Drop Local
- Rotate
- Scale
- Jitter
Each corruption is applied with five severity levels, resulting in a total of 4890 object-affordance pairings, comprising 17 affordance categories and 23 object categories with 2047 distinct object shapes.
Updates 📰
- [2024.12] - We have released our PIAD-C and LASO-C datasets! 🎉📂
Dataset and Code Release 🚀
We are excited to announce the release of our dataset and dataloader:
- Dataset: Available in the
PIAD-CandLASO-Cfiles 📜 - Dataloader: Available in the
dataset.pyfile 📜
Stay tuned! Further evaluation code will be coming soon. 🔧✨
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