Research map / 3D vision / 3D generation
Learning Physical Intuition of Block Towers by Example
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Jointly predict block masks and fall outcome rather than train fall classification alone | PhysNet on synthetic block towers · Synthetic block towers | fall accuracy (%): 89.1 vs 86.5 | Helped |
| Use PhysNet rather than DeepMask for future mask prediction | Synthetic block towers · Synthetic block towers | Mask IoU (%): 42.4 -> 75.4 | Helped |
| Use PhysNet rather than DeepMask for future mask prediction | Synthetic block towers · Synthetic block towers | Log likelihood/px: -0.299 -> -0.107 | Helped |
| Remove ImageNet pretraining from Googlenet (ablation) | Googlenet · Real block towers | fall accuracy (%): 69.0 ± 2.1 -> 59.2 ± 2.2 | Hurt |
| Remove ImageNet pretraining from Googlenet (ablation) | Googlenet · Synthetic block towers | fall accuracy (%): 86.5 -> 86.5 | No clear effect |
| Train without 4-block towers (ablation) | Googlenet · Synthetic 4-block towers | fall accuracy (%): 82.3 -> 71.3 | Hurt |
| Train without 3-block towers (ablation) | PhysNet · Synthetic 3-block towers | fall accuracy (%): 87.9 -> 84.5 | Hurt |
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