This is the official implementation of the paper. If you use MOEC-3D dataset or the code, please cite our work:
@inproceedings{wang2022evac3d,
title={EvAC3D: From event-based apparent contours to 3d models via continuous visual hulls},
author={Wang, Ziyun and Chaney, Kenneth and Daniilidis, Kostas},
booktitle={European conference on computer vision},
pages={284--299},
year={2022},
organization={Springer}
}A copy of the paper can be found here. For more information regarding the project, please visit our project page.
We release ground truth object ply files, events, calibration files and camera poses here.
The learning folder has the poses, events, masks that we used for training and testing. Here is what is in the learning folder. Folder event_files has preprocessed events. gt_scans contains all ground truth scanning. masks contains the object masks from the camera view (inside 1 and outside 0), which one can use to lable the Apparent Contour Events. The camera poses are obtained by looking up the current angle (pose_to_angle in the event h5 file). The camera pose is obtained by accessing the nearest angle key in the pose dictionary stored in poses_objects.pkl. pose_to_event gives the indices of each pose with respect to the event stream. Optionally, a slerp interpolation can be used to assign different poses to events through interpolation. Camera calibration files are given with camchain-recons_*.yaml files. This is obtained using Kalibr. For carving, we would need the intrinsics and distortion parameters to map events to rays in the metric world.
After the network has been trained, we save them into an intermediate format for carving. An example script for running such inference is in training_code/predict_events.py. The script saves events, predicted labels, cameras poses, calibration and camera timestamps into a .pkl file. This file is then used as the argument to --file in the carving code.
To run the carving code, simply run:
python --file [prediction pickle file] --calib [calibration file path]
In this folder, we provide examples labels and configurations for running carving_animals.py. This script generates the same .npz file format as carving_objects.py, which can be used for mesh extraction.
Given the carved volumes saved as npz files, You can run evaluation.py to convert soft occupancy to meshes. Some default paramters are given for two example objects.