--- license: apache-2.0 tags: - egocentric - exotenric - surgery - or - scene-graph - activity-understanding - gaze - hand --- # EgoExOR-HQ: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity Understanding [![Dataset](https://img.shields.io/badge/Data-4d5eff?style=for-the-badge&logo=huggingface&logoColor=ffc83d)](https://huggingface.co/datasets/TUM/EgoExOR) [![Code](https://img.shields.io/badge/Code-000000?style=for-the-badge&logo=github&logoColor=white)](https://github.com/ardamamur/EgoExOR) [![NeurIPS 2025](https://img.shields.io/badge/NeurIPS-2025-ff6b35?style=for-the-badge)](https://neurips.cc/) **EgoExOR-HQ** — This repository hosts the **enriched high-quality release** of the EgoExOR dataset. For scene graph generation code, benchmarks, and pretrained models, see the [main EgoExOR repository](https://github.com/ardamamur/EgoExOR). **Authors:** Ege Özsoy, Arda Mamur, Felix Tristram, Chantal Pellegrini, Magdalena Wysocki, Benjamin Busam, Nassir Navab ## ✨ What's New in EgoExOR-HQ This release adds: - **High-quality images** — 1344×1344 resolution (instead of 336×336) - **Raw depth images** — From external RGB-D cameras (instead of pre-merged point clouds), so you can build merged or per-camera point clouds for your use case - **Per-device audios** — Separate audio streams per microphone ## Overview 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:** RGB, gaze, hand tracking, audio from wearable glasses - **Exocentric:** RGB and depth from RGB-D cameras, ultrasound imagery - **Annotations:** 36 entities, 22 relations (568,235 triplets) for scene graph generation This dataset sets a new foundation for OR perception, offering a rich, multimodal resource for next-generation clinical perception. ## 🌟 Key Features - **Multiple modalities** — RGB video, audio (full waveform + per-frame snippets, per-device), eye gaze, hand tracking, raw depth, and scene graph annotations - **Time-synchronized streams** — All modalities aligned on a common timeline for precise cross-modal correlation - **High-resolution RGB** — 1344×1344 frames for fine-grained visual analysis - **Raw depth** — Build custom point clouds or depth-based models; depth from external RGB-D cameras only - **Per-device audio** — Separate microphone streams for spatial or multi-channel audio processing ## 📂 Dataset Structure The dataset is distributed as **phase-level HDF5 files** for efficient download: | File | Description | |------|-------------| | `miss_1.h5` | MISS procedure, phase 1 | | `miss_2.h5` | MISS procedure, phase 2 | | `miss_3.h5` | MISS procedure, phase 3 | | `miss_4.h5` | MISS procedure, phase 4 | To obtain a single merged file (including splits), use the merge utility from the [main EgoExOR repository](https://github.com/ardamamur/EgoExOR) (see `data/README.md`). ### HDF5 Schema ``` /metadata /vocabulary/entity — Entity names and IDs (instruments, anatomy, etc.) /vocabulary/relation — Relation names and IDs (holding, cutting, etc.) /sources/sources — Camera/source names and IDs (head_surgeon, external_1, etc.) /dataset — version, creation_date, title /procedures/{procedure}/phases/{phase}/takes/{take}/ /sources — source_count, source_0, source_1, … (camera roles) /frames/rgb — (num_frames, num_cameras, H, W, 3) uint8 — 1344×1344 /eye_gaze/coordinates — (num_frames, num_ego_cameras, 3) float32 — gaze 2D + camera ID /eye_gaze_depth/values — (num_frames, num_ego_cameras) float32 /hand_tracking/positions — (num_frames, num_ego_cameras, 17) float32 /audio/waveform — Full stereo waveform /audio/snippets — 1-second snippets aligned to frames /audio/per_device/ — Per-microphone waveform and snippets /point_cloud/depth/values — Raw depth images (external cameras; others zero-filled) /point_cloud/merged/ — Not populated; use raw depth to build point clouds yourself /annotations/ — Scene graph annotations (frame_idx, rel_annotations, scene_graph) /splits train, validation, test — Split tables (procedure, phase, take, frame_id) ``` **Note:** Camera/source IDs in `eye_gaze/coordinates` map to `metadata/sources` for correct source names. ## ⚙️ Efficiency and Usability - **HDF5** — Hierarchical structure, partial loading, gzip compression - **Chunking** — Efficient access to frame ranges for sequence-based training - **Logical layout** — `procedures → phases → takes → modality` for easy navigation ## 📜 License Released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). Free for academic and commercial use with attribution. ## 📚 Citation ```bibtex @misc{özsoy2025egoexoregoexocentricoperatingroom, title={EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity Understanding}, author={Ege Özsoy and Arda Mamur and Felix Tristram and Chantal Pellegrini and Magdalena Wysocki and Benjamin Busam and Nassir Navab}, year={2025}, eprint={2505.24287}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2505.24287}, } ``` ## 🔗 Related Resources - **Original EgoExOR (v1)** — [ardamamur/EgoExOR](https://huggingface.co/datasets/ardamamur/EgoExOR) — 336×336 images, pre-merged point clouds, merged audio - **Code, benchmarks, pretrained model** — [github.com/ardamamur/EgoExOR](https://github.com/ardamamur/EgoExOR) --- **Dataset:** [TUM/EgoExOR](https://huggingface.co/datasets/TUM/EgoExOR) · **Last Updated:** February 2025