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arxiv: 2605.24604 · v1 · pith:437I5DQInew · submitted 2026-05-23 · 💻 cs.CV

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

Gunwoo Jeon , Chaesong Park , Jongwoo Lim This is my paper

Pith reviewed 2026-06-30 14:06 UTC · model grok-4.3

classification 💻 cs.CV
keywords localflowmethodslc-flowconfidencecontinuouseventsframe-based
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The pith

LC-Flow maintains per-grid recurrent states to compute optical flow continuously from local events at arbitrary timestamps.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a learning method that estimates optical flow directly from the asynchronous local events produced by event cameras. It replaces both fixed-frame accumulation and stateless recomputation with a recurrent network that keeps a hidden state at every spatial grid location and updates it as new events arrive. The same network also outputs a per-estimate that reflects local reliability in the presence of sparsity or the aperture problem. These weighted local flows are then combined through a multi-scale aggregation step to produce a dense global field. On the MVSEC and DSEC benchmarks the approach matches or exceeds prior local methods and sets a new overall record on MVSEC.

Core claim

LC-Flow is the first temporally continuous learning-based optical flow estimator that operates purely from local events. A Continuous Local Recurrent Network maintains persistent hidden states per spatial grid, incrementally accumulating temporal context as events arrive. This produces sparse local flow estimates at arbitrary timestamps with full motion history. A jointly learned per-estimate filters unreliable outputs and supplies weights for a multi-scale confidence-guided aggregation that reconstructs globally consistent flow, achieving state-of-the-art performance among local methods on MVSEC and DSEC and a new overall state-of-the-art on MVSEC.

What carries the argument

Continuous Local Recurrent Network that maintains persistent hidden states per spatial grid and incrementally accumulates temporal context as events arrive, together with jointly learned scores used for filtering and aggregation.

If this is right

  • Flow estimates can be requested at any timestamp without waiting for a new accumulation window.
  • Each prediction carries explicit motion history from prior events rather than being recomputed statelessly.
  • scores allow downstream tasks such as visual odometry to discard unreliable local measurements.
  • The same scores provide principled weights that turn sparse local outputs into a globally consistent dense flow field.
  • The method reports higher accuracy than prior local approaches on MVSEC and DSEC and surpasses heavy frame-based networks on the overall MVSEC benchmark.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The architecture could support online visual odometry pipelines that update pose estimates continuously rather than at fixed intervals.
  • Similar per-location recurrent states might be applied to other asynchronous vision problems such as feature tracking or depth estimation from events.
  • The separation of local estimation from global aggregation suggests a modular design that could incorporate additional constraints like smoothness without retraining the entire network.
  • Testing on longer sequences with varying event rates would reveal whether hidden-state drift remains bounded in practice.
  • keywords:[

Load-bearing premise

A per-grid recurrent hidden state can be maintained and updated incrementally from sparse asynchronous events without drift or instability caused by the aperture problem or long gaps between events at a given location.

What would settle it

Accuracy of the continuous local estimates measured on event sequences containing long temporal gaps at individual grid locations, compared against the same network run with artificially shortened gaps.

Figures

Figures reproduced from arXiv: 2605.24604 by Chaesong Park, Gunwoo Jeon, Jongwoo Lim.

Figure 1
Figure 1. Figure 1: Overall architecture of LC-Flow. The Continuous Local Recurrent Network processes a localized event stream at grid g, where each event is embedded as elocal = (2(xi − xc)/K, 2(yi − yc)/K, (ti − ti−1) · α, pi), with K and α denoting the spatial and temporal scaling factors, respectively. The GRU maintains a persis￾tent hidden state hi per grid to continuously predict local flow and confidence. For global es… view at source ↗
Figure 2
Figure 2. Figure 2: Qualitative results on the MVSEC Indoor Flying 3 scene. From left to right: ground truth, ours, DCEIFlow [29], Multi-CM [27], and EVFlowNet [38]. Visualization is shown only at event-active pixels (i.e., pixels that received events within the evalu￾ation interval). 4.4 Effect of Confidence To validate the learned confidence measure, we analyze the correlation between predicted confidence scores and actual … view at source ↗
Figure 3
Figure 3. Figure 3: EPE and Outlier Ratio as a function of the confidence threshold on MVSEC sequences. Gray bars indicate pixel coverage. Errors decrease monotonically as the threshold increases, indicating that the learned confidence correlates with prediction quality [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: From left to right: Local flow, local confidence, confidence-guided aggregated flow, and ground truth on MVSEC sequences [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 1
Figure 1. Figure 1: Qualitative visualization of flow and confidence under different λ values on MVSEC. Rows correspond to λ = 0.1, 0.2, 0.4, 0.8 from top to bottom; columns show local flow, confidence map, and ground-truth flow from left to right. At λ = 0.4 and λ = 0.8, the confidence maps become near-uniform (saturated), losing the ability to discriminate reliable from unreliable predictions. (supervised baseline), and (ii… view at source ↗
Figure 2
Figure 2. Figure 2: Relative RMSE as a function of translation magnitude |t| on MVSEC in￾door_flying3. Both plots show that our method outperforms DCEIFlow. (a) Apply￾ing a stricter confidence threshold (0.3 vs. 0.0) consistently reduces translation error, confirming that learned confidence identifies high-quality flow. (b) The benefit of con￾fidence filtering is less pronounced for rotation, as rotational motion is inherentl… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces LC-Flow, a learning-based optical flow method for event cameras that uses a Continuous Local Recurrent Network to maintain persistent per-grid hidden states, enabling sparse local flow estimates at arbitrary timestamps with full temporal history. It jointly learns per-prediction confidence scores to handle sparsity and aperture ambiguity, using these for filtering and for multi-scale confidence-guided aggregation to produce dense flow. The method claims state-of-the-art results among local methods on MVSEC and DSEC, with the aggregated output setting a new overall SOTA on MVSEC.

Significance. If the recurrent stability and confidence calibration hold under the reported conditions, the work would meaningfully advance event-based vision by combining local operation with learned temporal continuity, moving beyond stateless model-based methods and latency-heavy frame-based ones. The dual use of confidence for both filtering and aggregation is a concrete engineering contribution, and the benchmark numbers (if robust to standard controls) would provide a useful reference point for future local continuous estimators.

major comments (3)
  1. [§3.2] §3.2 (Continuous Local Recurrent Network): The hidden-state update rule is described as incrementally accumulating context from asynchronous events, but no explicit decay, reset, or regularization term is introduced to bound error growth during long inter-event gaps at a given grid location. This directly bears on the central claim of stable 'full motion history' and on the reliability of the learned confidence scores.
  2. [§5.1, Table 3] §5.1 and Table 3 (MVSEC quantitative results): The reported overall SOTA after confidence-guided aggregation is presented without an ablation that isolates the contribution of the recurrent hidden state versus the aggregation step alone, or versus a non-recurrent local baseline with the same aggregation. This makes it difficult to attribute the gains to the temporally continuous component.
  3. [§4.3] §4.3 (confidence learning): The confidence is trained to quantify reliability under sparsity and aperture ambiguity, yet no quantitative evaluation (e.g., calibration plots or correlation with endpoint error on held-out sequences with controlled event density) is provided to verify that the scores actually track prediction quality rather than simply learning to down-weight difficult regions.
minor comments (2)
  1. [§3.2] Notation for the per-grid hidden state h_{i,j}(t) is introduced without an explicit statement of its dimensionality or initialization at t=0.
  2. [Figure 4] Figure 4 (qualitative results) would benefit from an additional column showing the per-pixel event density or inter-event gap duration to allow visual assessment of performance under the sparse regimes discussed in the text.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major point below and will make corresponding revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (Continuous Local Recurrent Network): The hidden-state update rule is described as incrementally accumulating context from asynchronous events, but no explicit decay, reset, or regularization term is introduced to bound error growth during long inter-event gaps at a given grid location. This directly bears on the central claim of stable 'full motion history' and on the reliability of the learned confidence scores.

    Authors: We acknowledge the absence of an explicit decay or regularization term in the hidden-state update. The network is trained end-to-end to maintain stable representations from data, with confidence scores designed to indicate unreliable predictions. To directly address the concern regarding long inter-event gaps, we will introduce a decay factor in the update rule and add analysis of its impact on error accumulation in the revised manuscript. revision: yes

  2. Referee: [§5.1, Table 3] §5.1 and Table 3 (MVSEC quantitative results): The reported overall SOTA after confidence-guided aggregation is presented without an ablation that isolates the contribution of the recurrent hidden state versus the aggregation step alone, or versus a non-recurrent local baseline with the same aggregation. This makes it difficult to attribute the gains to the temporally continuous component.

    Authors: We agree that an ablation isolating the recurrent component is necessary to attribute performance gains. In the revised version, we will add experiments comparing the full recurrent model to a non-recurrent local baseline, evaluated both with and without the confidence-guided aggregation step. revision: yes

  3. Referee: [§4.3] §4.3 (confidence learning): The confidence is trained to quantify reliability under sparsity and aperture ambiguity, yet no quantitative evaluation (e.g., calibration plots or correlation with endpoint error on held-out sequences with controlled event density) is provided to verify that the scores actually track prediction quality rather than simply learning to down-weight difficult regions.

    Authors: We will include quantitative evaluations of the learned confidence scores in the revision, specifically calibration plots and correlation analysis with endpoint error on held-out sequences under controlled event densities, to verify alignment with prediction quality. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation introduces independent recurrent architecture evaluated on external benchmarks

full rationale

The paper's central contribution is a Continuous Local Recurrent Network that maintains per-grid hidden states from asynchronous events, jointly learning confidence for aggregation. No equations, fitted parameters, or self-citations are shown that would make the claimed temporally continuous flow or SOTA results reduce to a definition or input by construction. Performance is reported on external benchmarks (MVSEC, DSEC) rather than internal fits, and the architecture is presented as a novel design choice without uniqueness theorems or ansatzes imported from prior self-work. The derivation chain is therefore self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no equations, training details, or modeling assumptions; ledger left empty pending full text.

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Reference graph

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