ToLL: Topological Layout Learning with Asymmetric Cross-View Structural Distillation for 3D Scene Graph Generation Pretraining
3D Scene Graph (3DSG) generation plays a pivotal role in spatial understanding and affordance perception. To mitigate generalization issues from data scarcity, joint-embedding and generative proxy tasks are proposed to pre-train 3DSG representations on predicate label-free datasets. Currently, generative pre-training usually bypasses the semantic corruption caused by the geometric augmentations in joint-embedding, but cannot avoid a negative problem ``Geometric Shortcut." In this problem, exposing dense object spatial and scale priors will induce models to trivially reconstruct scenes by interpolating object positions, rather than learning the underlying topological constraints provided b...
| Authors | Yucheng Huang, Luping Ji, Xiangwei Jiang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 March 2026 |
| Last updated at source | 20 April 2026 |
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