README.md

---
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