← 研踪 PaperTrailarXiv:2610.24462 · v2
O-SCD++: Replay-Free Online Scene Change Detection via Recursive Evidence Fusion
首次提交 2026-10-17 13:04
版本更新 2026-11-08 11:26
雷达观测 2026-11-10 12:16
摘要
Online scene change detection requires an autonomous agent to detect what has changed in a previously mapped environment while it is still on site, observing from unconstrained viewpoints and without access to future observations. This causal setting is strictly harder than offline detection, and online methods have long trailed offline accuracy. Our earlier method, O-SCD, closed that gap at over 10 frames per second, but its multi-view fusion is an iterative optimization that refits a change field over a 3D Gaussian splatting map of the reference scene to all past observations at every frame. This step consumes over half of the per-frame budget and forces accuracy to be traded against speed. We present O-SCD++, a replay-free formulation that eliminates this trade-off. Because the reference map stays fixed, rendering the change field is a linear operation. Each new observation can therefore be carried onto the map's Gaussians by a single backward pass of the renderer and fused by a two-scalar recursion per Gaussian. Past observations are never stored or revisited. O-SCD++ also makes the change/no-change decision without labels or iterative optimization. It sets each frame's threshold to the exact minimizer of O-SCD's self-supervised objective over threshold masks, using one sort, and the distance to this threshold gives a per-pixel decision margin that predicts errors. On PASLCD, O-SCD++ improves change detection accuracy (mIoU) from $0.489$ to $0.575$ and throughput from $11.8$ to $25.6$ frames per second, exceeding O-SCD's offline result while remaining causal.
作者备注：Project Page:https://chumsy0725.github.io/O-SCD/
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v2 · 2026-11-08 11:26 · 最新观测版本
O-SCD++: Replay-Free Online Scene Change Detection via Recursive Evidence Fusion
Online scene change detection requires an autonomous agent to detect what has changed in a previously mapped environment while it is still on site, observing from unconstrained viewpoints and without access to future observations. This causal setting is strictly harder than offline detection, and online methods have long trailed offline accuracy. Our earlier method, O-SCD, closed that gap at over 10 frames per second, but its multi-view fusion is an iterative optimization that refits a change field over a 3D Gaussian splatting map of the reference scene to all past observations at every frame. This step consumes over half of the per-frame budget and forces accuracy to be traded against speed. We present O-SCD++, a replay-free formulation that eliminates this trade-off. Because the reference map stays fixed, rendering the change field is a linear operation. Each new observation can therefore be carried onto the map's Gaussians by a single backward pass of the renderer and fused by a two-scalar recursion per Gaussian. Past observations are never stored or revisited. O-SCD++ also makes the change/no-change decision without labels or iterative optimization. It sets each frame's threshold to the exact minimizer of O-SCD's self-supervised objective over threshold masks, using one sort, and the distance to this threshold gives a per-pixel decision margin that predicts errors. On PASLCD, O-SCD++ improves change detection accuracy (mIoU) from $0.489$ to $0.575$ and throughput from $11.8$ to $25.6$ frames per second, exceeding O-SCD's offline result while remaining causal.
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