  Reconstructing intensity frames from event data while maintaining high
temporal resolution and dynamic range is crucial for bridging the gap between
event-based and frame-based computer vision. Previous approaches have depended
on supervised learning on synthetic data, which lacks interpretability and risk
over-fitting to the setting of the event simulator. Recently, self-supervised
learning (SSL) based methods, which primarily utilize per-frame optical flow to
estimate intensity via photometric constancy, has been actively investigated.
However, they are vulnerable to errors in the case of inaccurate optical flow.
This paper proposes a novel SSL event-to-video reconstruction approach, dubbed
EvINR, which eliminates the need for labeled data or optical flow estimation.
Our core idea is to reconstruct intensity frames by directly addressing the
event generation model, essentially a partial differential equation (PDE) that
describes how events are generated based on the time-varying brightness
signals. Specifically, we utilize an implicit neural representation (INR),
which takes in spatiotemporal coordinate $(x, y, t)$ and predicts intensity
values, to represent the solution of the event generation equation. The INR,
parameterized as a fully-connected Multi-layer Perceptron (MLP), can be
optimized with its temporal derivatives supervised by events. To make EvINR
feasible for online requisites, we propose several acceleration techniques that
substantially expedite the training process. Comprehensive experiments
demonstrate that our EvINR surpasses previous SSL methods by 38% w.r.t. Mean
Squared Error (MSE) and is comparable or superior to SoTA supervised methods.
Project page: https://vlislab22.github.io/EvINR/.
