The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data

Author:

Gu Cheng1,Learned-Miller Erik2,Sheldon Daniel2,Gallego Guillermo3,Bideau Pia1

Affiliation:

1. TU Berlin

2. UMass Amherst

3. TU Berlin & Einstein Center Digital Future

Funder

Deutsche Forschungsgemeinschaft

Publisher

IEEE

Reference36 articles.

1. A 1280x960 Dynamic Vision Sensor with a 4.95-?m pixel pitch and motion artifact minimization;suh;IEEE Int Symp Circuits Syst (ISCAS),2020

2. Poisson Point Processes

3. Event Cameras, Contrast Maximization and Reward Functions: An Analysis

4. Event-Based Motion Segmentation by Motion Compensation

5. Efficient Population Registration of 3D Data

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2. CMax-SLAM: Event-Based Rotational-Motion Bundle Adjustment and SLAM System Using Contrast Maximization;IEEE Transactions on Robotics;2024

3. E2NeRF: Event Enhanced Neural Radiance Fields from Blurry Images;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

4. Adaptive Global Decay Process for Event Cameras;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06

5. Progressive Spatio-temporal Alignment for Efficient Event-based Motion Estimation;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06

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