# A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation

arXiv: https://arxiv.org/abs/1804.01306 · HTML: https://mlsanity.com/paper/1804.01306/

## Key facts

- Cited by 178 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2018.

## Built on by (most cited first, 30 of 178)

- [Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks](https://mlsanity.com/paper/2106.01862/) (2021)
- [Event-aided Direct Sparse Odometry](https://mlsanity.com/paper/2204.07640/) (2022)
- [Secrets of Event-Based Optical Flow](https://mlsanity.com/paper/2207.10022/) (2022)
- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Learning Dense and Continuous Optical Flow from an Event Camera](https://mlsanity.com/paper/2211.09078/) (2022)
- [Robust e-NeRF: NeRF from Sparse & Noisy Events under Non-Uniform Motion](https://mlsanity.com/paper/2309.08596/) (2023)
- [Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers](https://mlsanity.com/paper/2307.04129/) (2023)
- [Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow](https://mlsanity.com/paper/2303.05214/) (2023)
- [Event Collapse in Contrast Maximization Frameworks](https://mlsanity.com/paper/2207.04007/) (2022)
- [TMA: Temporal Motion Aggregation for Event-based Optical Flow](https://mlsanity.com/paper/2303.11629/) (2023)
- [Globally-Optimal Contrast Maximisation for Event Cameras](https://mlsanity.com/paper/2206.05127/) (2022)
- [Lightweight Event-based Optical Flow Estimation via Iterative Deblurring](https://mlsanity.com/paper/2211.13726/) (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)
- [Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow](https://mlsanity.com/paper/2112.06242/) (2021)
- [The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data](https://mlsanity.com/paper/2106.06887/) (2021)
- [Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event Cameras](https://mlsanity.com/paper/2112.10591/) (2021)
- [E-MLB: Multilevel Benchmark for Event-Based Camera Denoising](https://mlsanity.com/paper/2303.11997/) (2023)
- [RPEFlow: Multimodal Fusion of RGB-PointCloud-Event for Joint Optical Flow and Scene Flow Estimation](https://mlsanity.com/paper/2309.15082/) (2023)
- [Fusion-FlowNet: Energy-Efficient Optical Flow Estimation using Sensor Fusion and Deep Fused Spiking-Analog Network Architectures](https://mlsanity.com/paper/2103.10592/) (2021)
- [Learning from Event Cameras with Sparse Spiking Convolutional Neural Networks](https://mlsanity.com/paper/2104.12579/) (2021)
- [Efficient Human Pose Estimation via 3D Event Point Cloud](https://mlsanity.com/paper/2206.04511/) (2022)
- [Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation](https://mlsanity.com/paper/2407.10802/) (2024)
- [Comparing Representations in Tracking for Event Camera-based SLAM](https://mlsanity.com/paper/2104.09887/) (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)
- [IncEventGS: Pose-Free Gaussian Splatting from a Single Event Camera](https://mlsanity.com/paper/2410.08107/) (2024)
- [Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics](https://mlsanity.com/paper/2209.11741/) (2022)
- [EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset](https://mlsanity.com/paper/2312.08220/) (2023)

## Abstract (arXiv)

We present a unifying framework to solve several computer vision problems with event cameras: motion, depth and optical flow estimation. The main idea of our framework is to find the point trajectories on the image plane that are best aligned with the event data by maximizing an objective function: the contrast of an image of warped events. Our method implicitly handles data association between the events, and therefore, does not rely on additional appearance information about the scene. In addition to accurately recovering the motion parameters of the problem, our framework produces motion-corrected edge-like images with high dynamic range that can be used for further scene analysis. The proposed method is not only simple, but more importantly, it is, to the best of our knowledge, the first method that can be successfully applied to such a diverse set of important vision tasks with event cameras.