# Focus Is All You Need: Loss Functions For Event-based Vision

## TL;DR

This paper introduces a set of 22 focus loss functions for event-based vision, enabling improved motion compensation and analysis of event camera data by leveraging traditional shape-from-focus techniques.

## Contribution

It presents a new taxonomy of focus loss functions for event cameras, demonstrating their effectiveness across multiple vision tasks and providing a comprehensive comparison of their performance.

## Key findings

- Variance, gradient, and Laplacian magnitudes are among the best loss functions.
- The proposed loss functions improve accuracy in rotational motion, depth, and optical flow estimation.
- The methods are efficient and suitable for real-time event camera applications.

## Abstract

Event cameras are novel vision sensors that output pixel-level brightness changes ("events") instead of traditional video frames. These asynchronous sensors offer several advantages over traditional cameras, such as, high temporal resolution, very high dynamic range, and no motion blur. To unlock the potential of such sensors, motion compensation methods have been recently proposed. We present a collection and taxonomy of twenty two objective functions to analyze event alignment in motion compensation approaches (Fig. 1). We call them Focus Loss Functions since they have strong connections with functions used in traditional shape-from-focus applications. The proposed loss functions allow bringing mature computer vision tools to the realm of event cameras. We compare the accuracy and runtime performance of all loss functions on a publicly available dataset, and conclude that the variance, the gradient and the Laplacian magnitudes are among the best loss functions. The applicability of the loss functions is shown on multiple tasks: rotational motion, depth and optical flow estimation. The proposed focus loss functions allow to unlock the outstanding properties of event cameras.