TY - GEN
T1 - SoccerNet-Depth
T2 - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024
AU - Leduc, Arnaud
AU - Cioppa, Anthony
AU - Giancola, Silvio
AU - Ghanem, Bernard
AU - Van Droogenbroeck, Marc
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Monocular Depth Estimation (MDE) is fundamental in sports video understanding, enhancing augmented graphics, scene understanding, and game state reconstruction. Despite remarkable progress in autonomous driving and indoor scene understanding, there is currently a lack of MDE datasets tailored for sports. Furthermore, most existing datasets only focus on single images, disregarding the temporal aspect. In this work, we introduce the first video dataset for MDE in sports, SoccerNet-Depth, focusing on football and basketball videos. In particular, we leverage the graphic engine from video games to automatically extract video sequences and their associated depth maps, making our dataset easily scalable. Furthermore, we benchmark and fine-tune several state-of-the-art MDE methods on our dataset. Our analysis shows that MDE in sports is far from being solved, making our dataset a perfect playground for future research. Dataset and codes: https://github.com/SoccerNet/sn-depth.
AB - Monocular Depth Estimation (MDE) is fundamental in sports video understanding, enhancing augmented graphics, scene understanding, and game state reconstruction. Despite remarkable progress in autonomous driving and indoor scene understanding, there is currently a lack of MDE datasets tailored for sports. Furthermore, most existing datasets only focus on single images, disregarding the temporal aspect. In this work, we introduce the first video dataset for MDE in sports, SoccerNet-Depth, focusing on football and basketball videos. In particular, we leverage the graphic engine from video games to automatically extract video sequences and their associated depth maps, making our dataset easily scalable. Furthermore, we benchmark and fine-tune several state-of-the-art MDE methods on our dataset. Our analysis shows that MDE in sports is far from being solved, making our dataset a perfect playground for future research. Dataset and codes: https://github.com/SoccerNet/sn-depth.
UR - http://www.scopus.com/inward/record.url?scp=85191762375&partnerID=8YFLogxK
U2 - 10.1109/CVPRW63382.2024.00333
DO - 10.1109/CVPRW63382.2024.00333
M3 - Conference contribution
AN - SCOPUS:85191762375
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 3280
EP - 3282
BT - Proceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024
PB - IEEE Computer Society
Y2 - 16 June 2024 through 22 June 2024
ER -