Update README.md

README.md

@@ -40,9 +40,7 @@ Official code of the paper "EgoExOR: An Egocentric–Exocentric Operating Room D
 
 - **Authors**: [Ege Özsoy][eo], [Arda Mamur][am], Felix Tristram, Chantal Pellegrini, Magdalena Wysocki, Benjamin Busam, [Nassir Navab][nassir]
 
-Operating rooms (ORs) demand precise coordination among surgeons, nurses, and equipment in a fast-paced, occlusion-heavy environment, necessitating advanced perception models to enhance safety and efficiency. We present EgoExOR, a pioneering dataset to propel surgical scene understanding by fusing first-person and third-person perspectives. Spanning 93 minutes (84,277 frames at 15 FPS) of two critical spine procedures—Ultrasound-Guided Needle Insertion and Minimally Invasive Spine Surgery—EgoExOR integrates egocentric data (RGB, gaze, hand tracking, audio) from Project Aria glasses, exocentric RGB/depth from Azure Kinect cameras, and ultrasound imagery. Its detailed scene graph annotations, covering 36 entities and 22 relations (~573,000 triplets), enable robust modeling of clinical interactions, supporting tasks like action recognition and robotic guidance. We evaluate EgoExOR with established OR perception models and a new tailored approach, showcasing its potential to advance surgical automation and skill analysis. EgoExOR redefines OR datasets, offering a rich, multimodal resource for next-generation clinical perception.
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+Operating rooms (ORs) demand precise coordination among surgeons, nurses, and equipment in a fast-paced, occlusion-heavy environment, necessitating advanced perception models to enhance safety and efficiency. Existing datasets either provide partial egocentric views or sparse exocentric multi-view context, but do not explore the comprehensive combination of both. We introduce EgoExOR, the first OR dataset and accompanying benchmark to fuse first-person and third-person perspectives. Spanning 94 minutes (84,553 frames at 15 FPS) of two emulated spine procedures, Ultrasound-Guided Needle Insertion and Minimally Invasive Spine Surgery, EgoExOR integrates egocentric data (RGB, gaze, hand tracking, audio) from wearable glasses, exocentric RGB and depth from RGB-D cameras, and ultrasound imagery. Its detailed scene graph annotations, covering 36 entities and 22 relations (568,235 triplets), enable robust modeling of clinical interactions, supporting tasks like action recognition and human-centric perception. We evaluate the surgical scene graph generation performance of two adapted state-of-the-art models and offer a new baseline that explicitly leverages EgoExOR’s multimodal and multi-perspective signals. This new dataset and benchmark set a new foundation for OR perception, offering a rich, multimodal resource for next-generation clinical perception.
 
 [am]: https://github.com/ardamamur/
 [eo]: https://www.cs.cit.tum.de/camp/members/ege-oezsoy/