SPARK Challenge : SPAcecraft Recognition leveraging Knowledge of Space Environment
We are happy to confirm the schedule for the SPARK2022 challenge! Please find below all the details.
- Registration opens: May 20
- Training + validation datasets (synthetic) release: June 1
- Test dataset (lab) release + leader board opens: June 30
- Challenge deadline: July 22
- Paper submission deadline: July 22
- Decision notification: August 15
- Camera Ready: August 20
Sponsorship & Awards
This year, 3000EUR will be awarded to the winners!
The team behind the Challenge
leads the Computer Vision, Imaging and Machine Intelligence (CVI2) research group at the Interdisciplinary 4Centre for Security, Reliability, and Trust (SnT) of the University of Luxembourg. She received the Ph.D. degree in electrical engineering in 2009 from North Carolina State University (NCSU). She has worked as a consultant for major research laboratories, i.e., LANL, Bell Labs., MERL. Her research interests include 3D shape modelling, RGB-D data enhancement, and multi-sensor fusion. She is the co-author of four IEEE best paper awards. Prof. Aouada is an IEEE Senior Member. She has served as Chair of the IEEE Benelux Women in Engineering Affinity Group (2014 – 2016), Chair of the SHApe Recovery from Partial textured 3D scans (SHARP) Workshop and Challenge in 2020 and in 2021, in conjunction with ECCV 2020, and CVPR 2021, respectively. She has also served as Area Chair at the 2020 International Conference on 3D Vision (3DV) and is the Program Chair at 3DV 2021.
Miguel Ortiz del Castillo
is a Research Associate at SnT, University of Luxembourg, and a member of the Computer Vision, Imaging and Machine Intelligence (CVI2) research group. He received his PhD degree in Computer Science from the University of Lorraine, France, in 2020. His PhD research was carried out at the French National Institute for Research in Digital Science and Technology (Inria). His research interests are in computer vision, focusing on object pose estimation and object-based camera pose estimation, through the use of generic object modeling and analytical geometry.
Mohamed Adel Musallam
is a 3rd year industrial PhD student in the CVI2 research group at SnT, working under the supervision of Prof. Djamila Aouada. Mohamed received his Master’s degree in computer vision from the University of Burgundy Franche-Comté (France), in 2019. His research interests are in Computer Vision and Deep Learning focusing on object recognition, pose estimation, and space situational awareness.