Table · dataset · 2025
Dynamic EventNeRF: Reconstructing General Dynamic Scenes from Multi-view RGB and Event Streams
Listed in Edmond
Volumetric reconstruction of dynamic scenes is an important problem in computer vision.
Description
It is especially challenging in poor lighting and with fast motion. This is partly due to limitations of RGB cameras: To capture frames under low lighting, the exposure time needs to be increased, which leads to more motion blur.
In contrast, event cameras, which record changes in pixel brightness asynchronously, are much less dependent on lighting, making them more suitable for recording fast motion. We hence propose the first method to spatiotemporally reconstruct a scene from sparse multi-view event streams and sparse RGB frames. We train a sequence of cross-faded time-conditioned NeRF models, one per short recording segment.
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The individual segments are supervised with a set of event- and RGB-based losses and sparse-view regularisation. We assemble a real-world multi-view camera rig with six static event cameras around the object and record a benchmark multi-view event stream dataset of challenging motions. Our work outperforms RGB-based baselines, producing state-of-the-art results, and opens up the topic of multi-view event-based reconstruction as a new path for fast scene capture beyond RGB cameras.
This dataset contains: 1) Synthetic scenes 2) Raw aedat4 event+RGB recordings and calibration 3) Processed event+RGB scenes to be used with model code
Links
Where it is published
- Dataverse dataset page edmond.mpg.de/dataset.xhtml?persistentId=doi%3A10.17617%2F3.AD2LQB ↗
landing page · from edmond mpdl mpg de
- DOI doi.org/10.17617/3.ad2lqb ↗
DOI / persistent id · from edmond mpdl mpg de
Catalogue records · 1
- Dataverse API edmond.mpg.de/api/datasets/:persistentId/?persistentId=doi%3A10.17617%2F3.AD… ↗
metadata API · from edmond mpdl mpg de
Topics
- Stated by source
- Computer Science, Systems and Electrical Engineering · Computer Science, Systems and Electrical Engineering
- From keywords
- Astronomy & Astrophysics · Chemistry · Computer Science & AI · Computer Science & AI · Earth & Environmental Science · Economics & Finance · Engineering · Engineering · Humanities · Life Sciences · Materials Science · Medicine & Health · Ocean & Atmospheric Science · Physics · Social Science
- Inferred from text
- Visual arts 70%
Provenance · 2 source records, 24 field assertions
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|---|---|---|---|
| Edmond | doi:10.17617/3.AD2LQB | 13 d ago | JSON v1 |
| Edmond | doi:10.17617/3.AD2LQB | 11 d ago | JSON v1 |
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