Summary of Evggs: a Collaborative Learning Framework For Event-based Generalizable Gaussian Splatting, by Jiaxu Wang et al.
EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting
by Jiaxu Wang, Junhao He, Ziyi Zhang, Mingyuan Sun, Jingkai Sun, Renjing Xu
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This event-based 3D reconstruction framework, called EvGGS, leverages the advantages of high dynamic range and low latency offered by event cameras for challenging lighting conditions and fast-moving scenarios. The framework consists of three modules: depth estimation, intensity reconstruction, and Gaussian regression, which are trained collaboratively with a joint loss to promote mutual improvement. This approach outperforms individual training and achieves better reconstruction quality compared to baselines, with satisfactory rendering speed. The proposed framework can generalize to unseen cases without retraining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Event cameras have the potential to revolutionize 3D reconstruction by offering high dynamic range and low latency. However, reconstructing scenes from raw event streams is challenging because event data is sparse and doesn’t carry absolute color information. A new framework called EvGGS can overcome this challenge by reconstructing scenes as 3D Gaussians in a feedforward manner and generalizing to unseen cases without retraining. The framework includes three modules that work together to estimate depth, intensity, and Gaussian parameters. |
Keywords
» Artificial intelligence » Depth estimation » Regression
