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arXiv Computer Vision By Jingxing Li, Anurag Paul, Subhasis Das, Cindy Xu

Decision-FLIP: Budget-Free Structured Pruning for Gaussian Occupancy Models

Read the original on arXiv Computer Vision →

Decision‑FLIP is a budget‑free pruning framework for Gaussian occupancy models that scores each persistent Gaussian row by how many voxel decisions change when the row is removed. It uses a guarded Otsu split on the log‑score distribution to select survivors without a target, then compiles the survivors into a smaller checkpoint, reducing bank size and latency. Across multiple model‑dataset settings, the method removes 12–79% of rows, cuts latency by 7–45%, and saves up to 8.0% of whole‑model compute while keeping semantic mIoU within 0.18 points of the original.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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