O-SCD++: Replay-Free Online Scene Change Detection via Recursive Evidence Fusion

Key points

• O-SCD closed that gap by treating change as a persistent property of a 3D Gaussian splatting map of the reference scene, surpassing the strongest offline methods in change detection accuracy while operating at over 10 frames per second.1
• O-SCD optimizes a change field over the Gaussians of the reference map, refitting it to all past cue maps at every frame, a step that consumes more than half of the per-frame budget and forces accuracy to be traded against speed.1
• We present a replay-free formulation that eliminates this trade-off.1
• On PASLCD, our method improves mean intersection over union from 0.489 to 0.575 and throughput from 11.8 to 25.6 frames per second, even exceeding O-SCD's offline result while remaining strictly causal.1

Sources (1)

• [1]O-SCD++: Replay-Free Online Scene Change Detection via Recursive Evidence Fusion
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 05:04 AM

  Online scene change detection requires an 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.

  O-SCD closed that gap by treating change as a persistent property of a 3D Gaussian splatting map of the reference scene, surpassing the strongest offline methods in change detection accuracy while operating at over 10 frames per second.

Extractive summary: sentences quoted from the sources.