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

arXiv: https://arxiv.org/abs/1904.07235 · HTML: https://mlsanity.com/paper/1904.07235/

## Key facts

- Cited by 83 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 30 of 83)

- [Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks](https://mlsanity.com/paper/2106.01862/) (2021)
- [Secrets of Event-Based Optical Flow](https://mlsanity.com/paper/2207.10022/) (2022)
- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation](https://mlsanity.com/paper/2111.12341/) (2021)
- [Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow](https://mlsanity.com/paper/2303.05214/) (2023)
- [DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions](https://mlsanity.com/paper/2202.02556/) (2022)
- [Event Collapse in Contrast Maximization Frameworks](https://mlsanity.com/paper/2207.04007/) (2022)
- [Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image Translation](https://mlsanity.com/paper/2109.01801/) (2021)
- [Globally-Optimal Contrast Maximisation for Event Cameras](https://mlsanity.com/paper/2206.05127/) (2022)
- [CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Maximization](https://mlsanity.com/paper/2403.08119/) (2024)
- [Fully neuromorphic vision and control for autonomous drone flight](https://mlsanity.com/paper/2303.08778/) (2023)
- [The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data](https://mlsanity.com/paper/2106.06887/) (2021)
- [E-MLB: Multilevel Benchmark for Event-Based Camera Denoising](https://mlsanity.com/paper/2303.11997/) (2023)
- [Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation](https://mlsanity.com/paper/2407.10802/) (2024)
- [ESL: Event-based Structured Light](https://mlsanity.com/paper/2111.15510/) (2021)
- [Spatiotemporal Registration for Event-based Visual Odometry](https://mlsanity.com/paper/2103.05955/) (2021)
- [Multi-Event-Camera Depth Estimation and Outlier Rejection by Refocused Events Fusion](https://mlsanity.com/paper/2207.10494/) (2022)
- [Event-based Stereo Depth Estimation: A Survey](https://mlsanity.com/paper/2409.17680/) (2024)
- [Neuromorphic Event-based Facial Expression Recognition](https://mlsanity.com/paper/2304.06351/) (2023)
- [Event-based Non-Rigid Reconstruction from Contours](https://mlsanity.com/paper/2210.06270/) (2022)
- [EvHandPose: Event-based 3D Hand Pose Estimation with Sparse Supervision](https://mlsanity.com/paper/2303.02862/) (2023)
- [Event-based Simultaneous Localization and Mapping: A Comprehensive Survey](https://mlsanity.com/paper/2304.09793/) (2023)
- [ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras](https://mlsanity.com/paper/2410.09374/) (2024)
- [A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework](https://mlsanity.com/paper/2212.07350/) (2022)
- [EV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks](https://mlsanity.com/paper/2304.07200/) (2023)
- [Enhancing Traffic Object Detection in Variable Illumination with RGB-Event Fusion](https://mlsanity.com/paper/2311.00436/) (2023)
- [Event-based Continuous Color Video Decompression from Single Frames](https://mlsanity.com/paper/2312.00113/) (2023)
- [Visual Odometry with an Event Camera Using Continuous Ray Warping and Volumetric Contrast Maximization](https://mlsanity.com/paper/2107.03011/) (2021)
- [Event-based Monocular Dense Depth Estimation with Recurrent Transformers](https://mlsanity.com/paper/2212.02791/) (2022)
- [A 5-Point Minimal Solver for Event Camera Relative Motion Estimation](https://mlsanity.com/paper/2309.17054/) (2023)

## Abstract (arXiv)

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.