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• Seen2Scene is the first flow matching-based approach that trains directly on incomplete, real-world 3D scans for scene completion and generation.method 95%
• Seen2Scene's visibility-guided flow matching, which masks unknown regions, enables effective learning from real-world, partial observations.method 90%
• Seen2Scene outperforms baseline methods (SG-NN, NKSR) in 3D scan completion accuracy.result 85%
• Seen2Scene produces more geometrically detailed and semantically coherent scenes in layout-conditioned generation compared to BlockFusion, LT3SD, and WorldGrow.result 80%
• Training without masked flow matching causes the model to generate holes in scenes, following the pattern of incomplete training scans.technical 90%
• Using CLIP embeddings instead of one-hot semantic labels improves generalization to diverse object layouts.technical 75%
• Seen2Scene can be adapted for text-to-scene generation by first generating a 3D object layout from the text description.method 85%
• The model's training on datasets with limited object categories (like 3D-FRONT) reduces its generalization ability to diverse, cluttered real-world layouts.limitation 70%

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