提出首个无需标注、视角无关的实时场景变化检测方法，速度超10帧/秒且精度超越离线方法。
Changes in Real Time: Online Scene Change Detection with Multi-View Fusion
- 通过自监督融合损失与多视角观测，实现无标签的在线变化检测。
- 在真实数据集上达到超过10帧/秒的推理速度，性能超越最优离线方法。
- 适合需要实时感知变化的机器人或AR应用，尤其适用于动态多视角环境。
在线场景变化检测（SCD）是一项极具挑战性的任务，要求智能体在不受限视角下实时感知场景变化。现有在线SCD方法的准确率远低于离线方法。本文提出首个姿态无关、无需标注且保证多视角一致性的在线SCD方法，在超过10 FPS的速率下运行，并达到了新的最先进性能，甚至超越了最佳离线方法。该方法引入一种新型自监督融合损失，从多个线索和观测中推断场景变化；采用基于PnP的快速姿态估计对齐参考场景；并设计了一种快速变化引导的更新策略，用于3D高斯点云场景表示。在复杂真实世界数据集上的大量实验表明，本方法显著优于各类在线与离线基线。
原文摘要 · Abstract (English)
Online Scene Change Detection (SCD) is an extremely challenging problem that requires an agent to detect relevant changes on the fly while observing the scene from unconstrained viewpoints. Existing online SCD methods are significantly less accurate than offline approaches. We present the first online SCD approach that is pose-agnostic, label-free, and ensures multi-view consistency, while operating at over 10 FPS and achieving new state-of-the-art performance, surpassing even the best offline approaches. Our method introduces a new self-supervised fusion loss to infer scene changes from multiple cues and observations, PnP-based fast pose estimation against the reference scene, and a fast change-guided update strategy for the 3D Gaussian Splatting scene representation. Extensive experiments on complex real-world datasets demonstrate that our approach outperforms both online and offline baselines.
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