# Real-Time Visual Obstruction Detection in Surgical Augmented Reality

arXiv: https://arxiv.org/abs/2608.00232 · HTML: https://mlsanity.com/paper/2608.00232/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 50 references that are in the map.
- First posted on arXiv 31 Jul 2026.

## Builds on (most cited first, 18 of 18)

- [An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models](https://mlsanity.com/paper/2403.06764/) (2024)
- [LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models](https://mlsanity.com/paper/2403.15388/) (2024)
- [LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding](https://mlsanity.com/paper/2410.17434/) (2024)
- [VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos](https://mlsanity.com/paper/2405.19209/) (2024)
- [SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow](https://mlsanity.com/paper/2405.14793/) (2024)
- [SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation](https://mlsanity.com/paper/2308.08746/) (2023)
- [MDP3: A Training-free Approach for List-wise Frame Selection in Video-LLMs](https://mlsanity.com/paper/2501.02885/) (2025)
- [Surgical-LVLM: Learning to Adapt Large Vision-Language Model for Grounded Visual Question Answering in Robotic Surgery](https://mlsanity.com/paper/2405.10948/) (2024)
- [A Stitch in Time Saves Nine: Small VLM is a Precise Guidance for Accelerating Large VLMs](https://mlsanity.com/paper/2412.03324/) (2024)
- [SurGen: Text-Guided Diffusion Model for Surgical Video Generation](https://mlsanity.com/paper/2408.14028/) (2024)
- [ViDDAR: Vision Language Model-Based Task-Detrimental Content Detection for Augmented Reality](https://mlsanity.com/paper/2501.12553/) (2025)
- [SurgSora: Object-Aware Diffusion Model for Controllable Surgical Video Generation](https://mlsanity.com/paper/2412.14018/) (2024)
- [LiftFeat: 3D Geometry-Aware Local Feature Matching](https://mlsanity.com/paper/2505.03422/) (2025)
- [FREE: Fast and Robust Vision Language Models with Early Exits](https://mlsanity.com/paper/2506.06884/) (2025)
- [Detecting Visual Information Manipulation Attacks in Augmented Reality: A Multimodal Semantic Reasoning Approach](https://mlsanity.com/paper/2507.20356/) (2025)
- [Navigate Biopsy with Ultrasound under Augmented Reality Device: Towards Higher System Performance](https://mlsanity.com/paper/2402.02414/) (2024)
- [Benchmarking Vision-Language Models under Contradictory Virtual Content Attacks in Augmented Reality](https://mlsanity.com/paper/2604.05510/) (2026)
- [Tetris: Tile-level Sampling for Efficient and High-Fidelity Video Object Tracking](https://mlsanity.com/paper/2605.25538/) (2026)

## Abstract (arXiv)

Surgical augmented reality (AR) can provide contextual guidance by overlaying virtual annotations, tool cues, and procedural information onto the surgical workspace. However, the virtual content may obstruct task-relevant real-world information, such as surgical instruments, and interfere with users' perception during time-sensitive surgical tasks. In this paper, we investigate visual obstruction detection for surgical AR and present a latency-aware pipeline that combines vision-language model (VLM)-based surgical-object recognition with segmentation-based obstruction reasoning. To reduce inference overhead, the system adopts a cascaded small-to-large VLM architecture with segmentation-guided early exiting and attention-based visual token pruning. The small VLM handles easy frames when its key-object prediction is supported by segmentation consistency, while difficult frames are forwarded to a large VLM with pruned visual tokens. We construct a pseudo-AR surgical obstruction detection benchmark by overlaying virtual content onto surgical-tool images and labeling whether the virtual content obstructs task-relevant instruments. Evaluation results show that the proposed system achieves 87.43% obstruction detection accuracy with an average end-to-end latency of 479 ms, reducing latency by 62.90% compared with a cloud large-model baseline. These results demonstrate the feasibility of latency-aware obstruction detection for surgical AR and motivate future work on dynamic surgical videos, multi-object scenes, and clinically grounded AR guidance content.