Dongyue Lu Lingdong Kong Tianxin Huang Gim Hee Lee National University of Singapore
## About ๐ ๏ธ **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**.