Contains 50 minutes of footage with both color frames and events. CED features a wide variety of indoor and outdoor scenes.
13 PAPERS • NO BENCHMARKS YET
The N-ImageNet dataset is an event-camera counterpart for the ImageNet dataset. The dataset is obtained by moving an event camera around a monitor displaying images from ImageNet. N-ImageNet contains approximately 1,300k training samples and 50k validation samples. In addition, the dataset also contains variants of the validation dataset recorded under a wide range of lighting or camera trajectories. Additional details about the dataset are explained in the paper available through this link. Please cite this paper if you make use of the dataset.
11 PAPERS • 3 BENCHMARKS
TUM-VIE is an event camera dataset for developing 3D perception and navigation algorithms. It contains handheld and head-mounted sequences in indoor and outdoor environments with rapid motion during sports and high dynamic range. TUM-VIE includes challenging sequences where state-of-the art VIO fails or results in large drift. Hence, it can help to push the boundary on event-based visual-inertial algorithms.
11 PAPERS • NO BENCHMARKS YET
In this work, we propose a general dataset for Color-Event camera based Single Object Tracking, termed COESOT. It contains 1354 color-event videos with 478,721 RGB frames. We split them into a training and testing subset, which contains 827 and 527 videos, respectively. The videos are collected from both outdoor and indoor scenarios (such as the street, zoo, and home) using the DAVIS346 event camera with a zoom lens. Therefore, our videos can reflect the variation in the distance at depth, but other datasets are failed to. Different from existing benchmarks which contain limited categories, our proposed COESOT covers a wider range of object categories (90 classes), as shown in Fig. 3 (a). It mainly reflects four groups, including persons, animals, electronics, and other goods.
9 PAPERS • 1 BENCHMARK
Large-scale single-object tracking dataset, containing 108 sequences with a total length of 1.5 hours. FE108 provides ground truth annotations on both the frame and event domain. The annotation frequency is up to 40Hz and 240Hz for the frame and event domains, respectively. FE108 is the largest event-frame-based dataset for single object tracking, and also offers the highest annotation frequency in the event domain.
8 PAPERS • 1 BENCHMARK
Event-Human3.6m is a challenging dataset for event-based human pose estimation by simulating events from the RGB Human3.6m dataset. It is built by converting the RGB recordings of Human3.6m into events and synchronising raw joints ground-truth with events frames through interpolation.
2 PAPERS • NO BENCHMARKS YET
Used to show systematic performance improvement in applications such as high frame-rate video synthesis, feature/corner detection and tracking, as well as high dynamic range image reconstruction.
We use Kubric and ESIM simulator to make our EKubric dataset, which has 15,367 RGB-PointCloud-Event pairs with annotations (including optical flow, scene flow, surface normal, semantic segmentation and object coordinates ground truths).
1 PAPER • NO BENCHMARKS YET
This synthetic event dataset is used in Robust e-NeRF to study the collective effect of camera speed profile, contrast threshold variation and refractory period on the quality of NeRF reconstruction from a moving event camera. It is simulated using an improved version of ESIM with three different camera configurations of increasing difficulty levels (i.e. easy, medium and hard) on seven Realistic Synthetic $360^{\circ}$ scenes (adopted in the synthetic experiments of NeRF), resulting in a total of 21 sequence recordings. Please refer to the Robust e-NeRF paper for more details.