ESAR: Event-Based Synthetic Aperture Reconstruction

2026-07-16 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

ESAR (Event-Based Synthetic Aperture Reconstruction) is a novel method that reformulates monocular event-based imaging as a synthetic-aperture inverse problem to reconstruct a static ground-domain log-radiance field, denoted as $θ$. Unlike traditional approaches that reconstruct a latent pixel-time volume $v$, ESAR imposes a geometric relation $v=Pθ$, where $P$ maps the fixed scene into motion-dependent latent views. The method aggregates events over finite time intervals, leading to a linearized model $APθ= b+η$, where $A$ is a temporal differencing operator and $b$ represents signed binned event counts. Despite the composite operator $AP$ being ill-conditioned due to combined spatial averaging and temporal differencing, ESAR employs regularized inversion to recover $θ$. Numerical experiments, conducted on both simulated data and real near-nadir Falcon Neuro event data, demonstrate that ESAR effectively recovers coherent large-scale spatial structure while suppressing fine-scale texture, outperforming dynamic latent-image and learned event-reconstruction baselines.

Key takeaway

For computer vision engineers developing robust scene reconstruction systems with event cameras, ESAR offers a principled approach to recover static scene radiance. You should consider this method for applications requiring coherent large-scale spatial structure from asynchronous event data, especially when traditional frame-based methods struggle with motion blur or dynamic range. This technique provides a strong baseline against dynamic latent-image and learned event-reconstruction methods.

Key insights

The ESAR method reconstructs static scene radiance from event camera data by formulating it as a synthetic-aperture inverse problem using regularized inversion.

Principles

• Event cameras capture log-radiance changes.
• Monocular event imaging can be an inverse problem.
• Regularized inversion handles ill-conditioned operators.

Method

Formulate event imaging as $APθ= b+η$, where $v=Pθ$ relates scene $θ$ to views $v$. Aggregate events into $b$, then use regularized inversion to recover $θ$.

In practice

• Recover large-scale spatial structure.
• Suppress fine-scale texture in reconstructions.
• Outperform dynamic latent-image baselines.

Topics

• Event Cameras
• Synthetic Aperture Reconstruction
• Inverse Problems
• Log-Radiance Field
• Image Reconstruction
• Falcon Neuro Data

Best for: Research Scientist, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.