LC-Flow: Learning Local Continuous Optical Flow and Confidence from events

• URL: http://arxiv.org/abs/2605.24604v1
• Date: Sat, 23 May 2026 14:42:24 GMT
• Title: LC-Flow: Learning Local Continuous Optical Flow and Confidence from events
• Abstract summary: Event cameras capture brightness changes asynchronously with microsecond resolution, yet existing optical flow methods fail to exploit this temporal continuity.<n>We propose textbfLC-Flow, the first temporally continuous, learning-based optical flow estimator that operates purely from local events.
• Score: 5.146238489510258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event cameras capture brightness changes asynchronously with microsecond resolution, yet existing optical flow methods fail to fully exploit this temporal continuity. Frame-based approaches impose artificial accumulation latency and suffer from domain overfitting, while model-based local methods operate statelessly, discarding temporal history between predictions and yielding inaccurate flows. We propose \textbf{LC-Flow}, the first temporally continuous, learning-based optical flow estimator that operates purely from local events. At its core, a Continuous Local Recurrent Network maintains persistent hidden states per spatial grid, incrementally accumulating temporal context as events arrive. Unlike frame-based methods constrained to fixed accumulation windows, and unlike stateless model-based methods that recompute motion from scratch at each step, LC-Flow produces sparse local flow estimates at arbitrary timestamps with full motion history. To address the inherent ambiguity of local observations, we jointly learn a confidence score that quantifies the reliability of each prediction, explicitly handling event sparsity and the aperture problem. This confidence serves a dual role: filtering unreliable estimates for downstream tasks such as visual odometry, and providing principled weights for a multi-scale confidence-guided aggregation that reconstructs globally consistent flow from the sparse local outputs. LC-Flow achieves state-of-the-art performance among local methods on both MVSEC and DSEC, while the confidence-guided aggregation establishes a new overall state-of-the-art on the MVSEC benchmark, surpassing heavy frame-based networks that rely on global spatial priors.

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