Not All Relations Rotate Alike: Transformation-Aware Decoupling for Viewpoint-Robust 3D Scene Graph Generation
arXiv:2606.27412v1 Announce Type: cross Abstract: 3D Scene Graph Generation (3DSGG) represents 3D scenes as structured object-relation-object graphs, providing a compact relational abstraction for spatial understanding. In embodied intelligence settings, the same 3D scene may be observed by agents from viewpoints that differ by yaw rotations. However, current 3DSGG models often fail to produce relation predictions that follow the expected transformation behavior under such viewpoint shifts. This behavior reveals an empirical mismatch related to predicate-level transformation heterogeneity: dir
This research addresses fundamental challenges in 3D scene understanding for embodied AI, a critical area as AI systems move into physical environments.
Improved 3D scene graph generation that is robust to viewpoint changes is essential for reliable navigation, manipulation, and interaction of autonomous agents in dynamic real-world settings.
The ability of AI systems to maintain consistent understanding of their environment despite changes in observation viewpoint will improve, enhancing their reliability and autonomy in physical spaces.
- · Embodied AI developers
- · Robotics companies
- · Logistics and automation sector
- · Companies relying on less sophisticated 3D vision systems
Embodied AI agents become more effective at perceiving and interacting with complex 3D environments.
This leads to faster deployment and broader adoption of autonomous robots in sectors like warehousing, manufacturing, and service.
Increased reliability of embodied AI could accelerate the development of general-purpose humanoid robots and advanced assistive technologies.
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Read at arXiv cs.AI