  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. Our proposed
method, ESGNN, outperforms existing state-of-the-art approaches, demonstrating
a significant improvement in scene estimation with faster convergence. ESGNN
demands low computational resources and is easy to implement from available
frameworks, paving the way for real-time applications such as robotics and
computer vision.
