Datasets:

TUM
/

EgoExOR

+---
+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.
+## 🔗 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