## Event-3DGS: Event-based 3D Reconstruction Using 3D Gaussian Splatting

This is the official implementation of Event3DGS. The readme.md will be further improved after the paper is accepted and the code is officially published.

### (1) Train

you can also use **Train.py** like original 3DGS,  except some additional command line such as: **--event, --gray, --ColorEvent, --Deblur**

now it supports Colmap format, with an additional **images_event** folder( which has estimated intensity). For Deblur and ColorEvent, the data format would be more complex.

a classic cmd of training in vscode:

######        "args": ["-s", "/slider_depth_colmap_easy","--gray","--event","--iterations","8000","-m","//slider_depth_colmap_easy/FinalCom","--start_checkpoint","/slider_depth_colmap_easy/initial/chkpnt999.pth"],

the dataset used in the paper will be released after the paper is accepted because it is quite large.



### (2) eval

After training, you can load a model and use Train to get the specific image in a camera pose.

You can use SIBR_VIEWER  to observe the output 3D scene in real time.



### (3) other

we have changed and added a lot of other files, like dataset_utils/*,  render.py, loss_utils.py, and so on. We have also added a function that enables depth map generation



### Acknowledgment

- Part of our code and dataset refers :

  https://github.com/graphdeco-inria/gaussian-splatting

  https://github.com/cedric-scheerlinck/jupnote_event_demo

  https://rpg.ifi.uzh.ch/davis_data.html

  https://github.com/vsitzmann/deepvoxels
  
  https://github.com/neuromorphicsystems/IEBCS/blob/main/LICENSE

