NoPA replaces Gaussian object approximations with non-parametric distributions and MMD-based merging to improve accuracy in real-time 3D scene graph generation.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
DeWorldSG improves 3D scene graph generation from RGB-D sequences by using depth-guided 3D Gaussian object nodes and V-JEPA 2 world-model priors for spatiotemporal relation refinement, reporting large recall gains on 3DSSG and ReplicaSSG.
citing papers explorer
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NoPA: Non-Parametric Online 3D Scene Graph Generation
NoPA replaces Gaussian object approximations with non-parametric distributions and MMD-based merging to improve accuracy in real-time 3D scene graph generation.
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DeWorldSG: Depth-Aware 3D Semantic Scene Graph Generation via World-Model Priors
DeWorldSG improves 3D scene graph generation from RGB-D sequences by using depth-guided 3D Gaussian object nodes and V-JEPA 2 world-model priors for spatiotemporal relation refinement, reporting large recall gains on 3DSSG and ReplicaSSG.