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• The proposed ToLL framework outperforms state-of-the-art baselines on the 3DSSG dataset.result 90%
• Current cross-modal assisted representation learning methods for 3D Scene Graph generation rely heavily on predicate annotations, limiting generalization.limitation 85%
• The Anchor-Conditioned Topological Geometry Reasoning (ACTGR) module enforces predicate relation learning by strictly modulating the layout recovery process with predicate features.method 80%
• Structural Multi-view Augmentation (SMA) avoids semantic corruption and enhances representations via self-distillation.method 85%
• ToLL provides a label-free and robust self-supervised proxy task for 3D Scene Graph fine-tuning.technical 80%
• The GNN in the ACTGR module functions as a rigorous 'dead reckoning' system to ensure learned features reflect cumulative geometric transformations rather than local memorization.technical 75%
• Self-supervised learning methods for point clouds struggle in the 3D Scene Graph domain due to rigid geometric augmentations.limitation 70%
• ToLL's proxy task is a non-trivial scene generation via object-geometric bottleneck, unlike trivial scene generation or cross-modal alignment tasks.method 85%

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