  Event cameras offer promising advantages such as high dynamic range and low
latency, making them well-suited for challenging lighting conditions and
fast-moving scenarios. However, reconstructing 3D scenes from raw event streams
is difficult because event data is sparse and does not carry absolute color
information. To release its potential in 3D reconstruction, we propose the
first event-based generalizable 3D reconstruction framework, called EvGGS,
which reconstructs scenes as 3D Gaussians from only event input in a
feedforward manner and can generalize to unseen cases without any retraining.
This framework includes a depth estimation module, an intensity reconstruction
module, and a Gaussian regression module. These submodules connect in a
cascading manner, and we collaboratively train them with a designed joint loss
to make them mutually promote. To facilitate related studies, we build a novel
event-based 3D dataset with various material objects and calibrated labels of
grayscale images, depth maps, camera poses, and silhouettes. Experiments show
models that have jointly trained significantly outperform those trained
individually. Our approach performs better than all baselines in reconstruction
quality, and depth/intensity predictions with satisfactory rendering speed.
