ESGNN: Towards Equivariant Scene Graph Neural Network for 3D Scene Understanding

Understand

Scene graphs have been proven to be useful for various scene understanding tasks due to their compact and explicit nature.

• However, existing approaches often neglect the importance of maintaining the symmetry-preserving property when generating scene graphs from 3D point clouds.
• This oversight can diminish the accuracy and robustness of the resulting scene graphs, especially when handling noisy, multi-view 3D data.
• This work, to the best of our knowledge, is the first to implement an Equivariant Graph Neural Network in semantic scene graph generation from 3D point clouds for scene understanding.