Publication: Dense Continuous-Time Optical Flow from Event Cameras
Dense Continuous-Time Optical Flow from Event Cameras
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Gehrig, M., Muglikar, M., & Scaramuzza, D. (2024). Dense Continuous-Time Optical Flow from Event Cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–12. https://doi.org/10.1109/TPAMI.2024.3361671
Abstract
Abstract
Abstract
We present a method for estimating dense continuous-time optical flow from event data. Traditional dense optical flow methods compute the pixel displacement between two images. Due to missing information, these approaches cannot recover the pixel trajectories in the blind time between two images. In this work, we show that it is possible to compute per-pixel, continuous-time optical flow using events from an event camera. Events provide temporally fine-grained information about movement in pixel space due to their asynchronous nature
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- Gehrig, Mathias
- Muglikar, Manasi
- Scaramuzza, Davide
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Citations
Gehrig, M., Muglikar, M., & Scaramuzza, D. (2024). Dense Continuous-Time Optical Flow from Event Cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–12. https://doi.org/10.1109/TPAMI.2024.3361671
