# 4D-OR: Semantic Scene Graphs for OR Domain Modeling

arXiv: https://arxiv.org/abs/2203.11937 · HTML: https://mlsanity.com/paper/2203.11937/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 29 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 22 of 22)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Image Generation from Scene Graphs](https://mlsanity.com/paper/1804.01622/) (2018)
- [3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera](https://mlsanity.com/paper/1910.02527/) (2019)
- [Action Genome: Actions as Composition of Spatio-temporal Scene Graphs](https://mlsanity.com/paper/1912.06992/) (2019)
- [Group-Free 3D Object Detection via Transformers](https://mlsanity.com/paper/2104.00678/) (2021)
- [Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions](https://mlsanity.com/paper/2004.03967/) (2020)
- [3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans](https://mlsanity.com/paper/2002.06289/) (2020)
- [TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks](https://mlsanity.com/paper/2003.10751/) (2020)
- [Surgical Data Science: Enabling Next-Generation Surgery](https://mlsanity.com/paper/1701.06482/) (2017)
- [Semantic Image Manipulation Using Scene Graphs](https://mlsanity.com/paper/2004.03677/) (2020)
- [MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation](https://mlsanity.com/paper/1808.08180/) (2018)
- [Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos](https://mlsanity.com/paper/1812.01366/) (2018)
- [Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery](https://mlsanity.com/paper/2006.16166/) (2020)
- [Concurrent Segmentation and Localization for Tracking of Surgical Instruments](https://mlsanity.com/paper/1703.10701/) (2017)
- [A Robotic 3D Perception System for Operating Room Environment Awareness](https://mlsanity.com/paper/2003.09487/) (2020)
- [Active Learning using Deep Bayesian Networks for Surgical Workflow Analysis](https://mlsanity.com/paper/1811.03382/) (2018)

## Built on by (most cited first, 30 of 49)

- [EchoScene: Indoor Scene Generation via Information Echo over Scene Graph Diffusion](https://mlsanity.com/paper/2405.00915/) (2024)
- [CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion](https://mlsanity.com/paper/2305.16283/) (2023)
- [Incremental 3D Semantic Scene Graph Prediction from RGB Sequences](https://mlsanity.com/paper/2305.02743/) (2023)
- [Latent Graph Representations for Critical View of Safety Assessment](https://mlsanity.com/paper/2212.04155/) (2022)
- [4D Panoptic Scene Graph Generation](https://mlsanity.com/paper/2405.10305/) (2024)
- [LABRAD-OR: Lightweight Memory Scene Graphs for Accurate Bimodal Reasoning in Dynamic Operating Rooms](https://mlsanity.com/paper/2303.13293/) (2023)
- [Dynamic Scene Graph Representation for Surgical Video](https://mlsanity.com/paper/2309.14538/) (2023)
- [Encoding Surgical Videos as Latent Spatiotemporal Graphs for Object and Anatomy-Driven Reasoning](https://mlsanity.com/paper/2312.06829/) (2023)
- [ORacle: Large Vision-Language Models for Knowledge-Guided Holistic OR Domain Modeling](https://mlsanity.com/paper/2404.07031/) (2024)
- [Syn-Mediverse: A Multimodal Synthetic Dataset for Intelligent Scene Understanding of Healthcare Facilities](https://mlsanity.com/paper/2308.03193/) (2023)
- [VISAGE: Video Synthesis using Action Graphs for Surgery](https://mlsanity.com/paper/2410.17751/) (2024)
- [Compile Scene Graphs with Reinforcement Learning](https://mlsanity.com/paper/2504.13617/) (2025)
- [EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity Understanding](https://mlsanity.com/paper/2505.24287/) (2025)
- [DisguisOR: Holistic Face Anonymization for the Operating Room](https://mlsanity.com/paper/2307.14241/) (2023)
- [Dynamic Depth-Supervised NeRF for Multi-View RGB-D Operating Room Images](https://mlsanity.com/paper/2211.12436/) (2022)
- [ST(OR)2: Spatio-Temporal Object Level Reasoning for Activity Recognition in the Operating Room](https://mlsanity.com/paper/2312.12250/) (2023)
- [Tri-modal Confluence with Temporal Dynamics for Scene Graph Generation in Operating Rooms](https://mlsanity.com/paper/2404.09231/) (2024)
- [Beyond Role-Based Surgical Domain Modeling: Generalizable Re-Identification in the Operating Room](https://mlsanity.com/paper/2503.13028/) (2025)
- [Statistical Confidence Rescoring for Robust 3D Scene Graph Generation from Multi-View Images](https://mlsanity.com/paper/2508.06546/) (2025)
- [When do they StOP?: A First Step Towards Automatically Identifying Team Communication in the Operating Room](https://mlsanity.com/paper/2502.08299/) (2025)
- [Location-Free Scene Graph Generation](https://mlsanity.com/paper/2303.10944/) (2023)
- [Specialized Foundation Models for Intelligent Operating Rooms](https://mlsanity.com/paper/2505.12890/) (2025)
- [Decoding the Surgical Scene: A Scoping Review of Scene Graphs in Surgery](https://mlsanity.com/paper/2509.20941/) (2025)
- [AI-Enhanced Virtual Reality in Medicine: A Comprehensive Survey](https://mlsanity.com/paper/2402.03093/) (2024)
- [TeleOR: Real-time Telemedicine System for Full-Scene Operating Room](https://mlsanity.com/paper/2407.19763/) (2024)
- [TrackOR: Towards Personalized Intelligent Operating Rooms Through Robust Tracking](https://mlsanity.com/paper/2508.07968/) (2025)
- [Towards Next-Generation Healthcare: A Survey of Medical Embodied AI for Perception, Decision-Making, and Action](https://mlsanity.com/paper/2606.15647/) (2026)
- [SSMNBench: Diagnosing Image-based Cross-View Human-Object Understanding via Single-View Sufficiency and Multi-View Necessity](https://mlsanity.com/paper/2606.25634/) (2026)
- [PoseAction: Action Recognition for Patients in the Ward using Deep Learning Approaches](https://mlsanity.com/paper/2310.03288/) (2023)
- [S^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR](https://mlsanity.com/paper/2402.14461/) (2024)

## Abstract (arXiv)

Surgical procedures are conducted in highly complex operating rooms (OR), comprising different actors, devices, and interactions. To date, only medically trained human experts are capable of understanding all the links and interactions in such a demanding environment. This paper aims to bring the community one step closer to automated, holistic and semantic understanding and modeling of OR domain. Towards this goal, for the first time, we propose using semantic scene graphs (SSG) to describe and summarize the surgical scene. The nodes of the scene graphs represent different actors and objects in the room, such as medical staff, patients, and medical equipment, whereas edges are the relationships between them. To validate the possibilities of the proposed representation, we create the first publicly available 4D surgical SSG dataset, 4D-OR, containing ten simulated total knee replacement surgeries recorded with six RGB-D sensors in a realistic OR simulation center. 4D-OR includes 6734 frames and is richly annotated with SSGs, human and object poses, and clinical roles. We propose an end-to-end neural network-based SSG generation pipeline, with a rate of success of 0.75 macro F1, indeed being able to infer semantic reasoning in the OR. We further demonstrate the representation power of our scene graphs by using it for the problem of clinical role prediction, where we achieve 0.85 macro F1. The code and dataset will be made available upon acceptance.