Decision-FLIP: Budget-Free Structured Pruning for Gaussian Occupancy Models
Occupancy predictors rely on large persistent banks of Gaussians or learned queries whose size is fixed by hand.
ProofPaper ↗
Key points
- Common pruning proxies, such as opacity, activation frequency, or parameter magnitude, do not measure whether removing a row changes the model's voxel decisions.
- We introduce Decision-FLIP, a budget-free compilation framework whose scoring and structure selection need only unlabeled calibration inputs.
- Under matched one-epoch recovery controls, semantic mIoU changes by at most 0.18 point.
- Same-$K$ controls show that the ranking, not only the bank size, matters: random removal collapses GaussianFormer-1 and OccFormer, while label-free Mean-FLIP matches or outperforms supervised Taylor ranking on all five settings where that control was run.
Sources (1)
- [1]Decision-FLIP: Budget-Free Structured Pruning for Gaussian Occupancy ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 08:48 PM
Occupancy predictors rely on large persistent banks of Gaussians or learned queries whose size is fixed by hand.
Common pruning proxies, such as opacity, activation frequency, or parameter magnitude, do not measure whether removing a row changes the model's voxel decisions.
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