DSEC: A Stereo Event Camera Dataset for Driving Scenarios
By Mathias Gehrig, Willem Aarents, Daniel Gehrig, Davide Scaramuzza. IEEE Robotics and Automation Letters, 2021
549 citations recorded by the source corpus.
Once an academic venture, autonomous driving has received unparalleled corporate funding in the last decade. Still, operating conditions of current autonomous cars are mostly restricted to ideal scenarios. This means that driving in challenging illumination conditions such as night, sunrise, and sunset remains an open problem. In these cases, standard cameras are being pushed to their limits in terms of low light and high dynamic range performance. To address these challenges, we propose, DSEC, a new dataset that contains such demanding illumination conditions and provides a rich set of sensory data. DSEC offers data from a wide-baseline stereo setup of two color frame cameras and two high-resolution monochrome event cameras. In addition, we collect lidar data and RTK GPS measurements, both hardware synchronized with all camera data. One of the distinctive features of this dataset is the inclusion of high-resolution event cameras. Event cameras have received increasing attention for their high temporal resolution and high dynamic range performance. However, due to their novelty, event camera datasets in driving scenarios are rare. This work presents the first high resolution, large scale stereo dataset with event cameras. The dataset contains 53 sequences collected by driving in a variety of illumination conditions and provides ground truth disparity for the development and evaluation of event-based stereo algorithms.
Subfields: Electrical and Electronic Engineering, Instrumentation
View Paper: https://www.semanticscholar.org/paper/ceab4559736c6ba710191b12ed7f6123b2f85131
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In Electrical and Electronic Engineering
- Wei Dang Lu
- Mohammed Bennamoun
- Xinxin Wang
- Zhipeng Cai
- Girish Dwivedi
- Emre O. Neftci
- Doo Seok Jeong
- Gregor Lenz
In Instrumentation
Citations in the corpus
- Metric3D v2: A Versatile Monocular Geometric Foundation Model for Zero-Shot Metric Depth and Surface Normal Estimation (2024, unknown): “12, including comic-style (Row 2) and CG(computer graphics)-generated objects (Row5)”
- Training Spiking Neural Networks Using Lessons From Deep Learning (2021, background): “DSEC [104] A stereo event camera dataset for driving scenarios.”
- Depth Anything 3: Recovering the Visual Space from Any Views (2025, unknown): “We trained our metric depth model on 14 datasets, including Taskonomy [120], DIML (Outdoor) [15], DDAD [30], Argoverse [101], Lyft [], PandaSet [105], Waymo [84], ScanNet++ [117], ARK-itScenes [5], Map-free [3], DSEC [24], Driving Stereo [109] and Cityscapes [16] datasets.”
- Low-latency automotive vision with event cameras (2024, unknown): “For this reason, several methods have combined events and frames for moving-object detections 79 , tracking 80 , computational photography 22,81,82 and monocular depth estimation 40 .”
- Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image (2023, methodology): “Training Data DDAD [19] Outdoor LiDar ∼80K 36+ Lyft [24] Outdoor LiDar ∼50K 6+ Driving Stereo (DS) [62] Outdoor Stereo† ∼181K 1 DIML [9] Outdoor Stereo† ∼122K 10 Arogoverse2 [55] Outdoor LiDar ∼3515K 6+ Cityscapes [10] Outdoor Stereo† ∼170K 1 DSEC [16] Outdoor LiDar ∼26K 1 Mapillary PSD [34] Outdoor”