SynthEVox3D-Tiny (Synthetic Event Camera Voxel 3D Reconstruction Dataset)

Event cameras are sensors that are inspired by biological systems and specialize in capturing changes in brightness. These emerging cameras offer numerous advantages over conventional frame-based cameras, including high dynamic range, high frame rates, and extremely low power consumption. As a result, event cameras are increasingly being used in various fields, such as object detection and tracking, autonomous driving, 3D reconstruction, visual odometry, and SLAM.

We have created the first large-scale synthetic event camera voxel 3D reconstruction dataset, comprising over 39,739 simulated event camera 3D object scans from 13 different object categories. Each entry in the dataset contains a 0.5-second, 240fps high frame rate RGB video scan, simulated event camera data, the original 3D model, and a converted 32x32x32 voxel model.

The 3D models used in this dataset are from ShapeNet (Link: https://shapenet.org/).

Although this dataset only provides voxel representation for ground truth, obtaining other types of representation such as point cloud as ground truth will be trivial with the provided gltf 3D model. We hope that by publishing this dataset, we can accelerate the advancement of event-based 3D reconstruction.

The original paper is available at:

IEEE Xplore - https://ieeexplore.ieee.org/document/10169359

ArXiv - https://arxiv.org/abs/2309.00385

!!! Due to limited resources, we are unable to release the full dataset, which is around 1.2 TB in size. We would greatly appreciate any organizations willing to host the full dataset for us. (Contact: haodong.chen@sydney.edu.au)

!!! The released SynthEVox3D-Tiny dataset, which is the dataset used in the original paper, is around 32 GB. We also provide scripts in the utils folder of the dataset to reproduce our results, making it possible for other researchers to recreate the entire dataset from scratch.