arXiv:2607.00889v1 Announce Type: cross Abstract: We present DeWorldSG, a novel framework that generates spatio-temporally robust 3D Semantic Scene Graphs from RGB-D sequences. Existing methods often struggle to construct reliable 3D scene graphs due to unstable 3D object representations and missing relations caused by frame-wise inference. DeWorldSG addresses these issues by estimating instance-level geometric 3D Gaussian distributions through depth-guided filtering and representing each object as a probabilistic 3D node rather than a single projected point. To mitigate relational sparsity fr
The continuous advancements in AI and computer vision are driving the need for more robust and reliable 3D scene understanding, especially with the proliferation of RGB-D sensors.
This development significantly enhances the ability of AI systems to comprehend and interact with the physical world, which is crucial for applications ranging from robotics to augmented reality.
AI systems can now build more stable and comprehensive 3D representations of environments, moving beyond frame-wise inference limitations to generate spatio-temporally robust scene graphs.
- · Robotics industry
- · Augmented/Virtual Reality (AR/VR)
- · Autonomous systems developers
- · Computer Vision researchers
- · Developers relying on unstable 3D reconstruction
- · Methods limited to 2D scene understanding
- · Systems with high reliance on perfect sensor data
- · Manual scene graph generation tools
Improved situational awareness and interaction capabilities for automated systems in complex environments.
Accelerated development and deployment of more sophisticated AI applications requiring deep spatial comprehension.
Enhanced safety and efficiency in human-robot collaboration and autonomous navigation due to superior environmental understanding.
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Read at arXiv cs.AI