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.

该论文通过引入等变图神经网络 (ESGNN) 到三维点云生成语义场景图中，实现了对场景的理解和估计的显著提升，并在速度和收敛性上具备优势，为机器人技术和计算机视觉等实时应用铺平了道路。

ESGNN：面向三维场景理解的等变场景图神经网络

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

Deep learning based localization and mapping has recently attracted
significant attention. Instead of creating hand-designed algorithms through
exploitation of physical models or geometric theories, deep learning based
solutions provide an alternative to solve the problem in a data-driven way.
Benefiting from ever-increasing volumes of data and computational power, these
methods are fast evolving into a new area that offers accurate and robust
systems to track motion and estimate scenes and their structure for real-world
applications. In this work, we provide a comprehensive survey, and propose a
new taxonomy for localization and mapping using deep learning. We also discuss
the limitations of current models, and indicate possible future directions. A
wide range of topics are covered, from learning odometry estimation, mapping,
to global localization and simultaneous localization and mapping (SLAM). We
revisit the problem of perceiving self-motion and scene understanding with
on-board sensors, and show how to solve it by integrating these modules into a
prospective spatial machine intelligence system (SMIS). It is our hope that
this work can connect emerging works from robotics, computer vision and machine
learning communities, and serve as a guide for future researchers to apply deep
learning to tackle localization and mapping problems.

本研究对以深度学习为基础的定位和建图方法进行全面调查和分类，讨论了目前模型的局限性，并预示了未来的方向。文中涵盖了从学习里程估计、绘图到全局定位和同时定位和绘图（SLAM）等广泛的话题，探讨了利用车载传感器来感知自我动作和场景理解的问题，并展示了如何将这些模块集成到 SMIS 中。希望本研究能连接机器人学、计算机视觉和机器学习社区的新兴工作，并为未来研究者提供指南，应用深度学习来解决定位和绘图问题。