AI Assisted AR Assembly: Object Recognition and Computer Vision for Augmented Reality Assisted Assembly (2511.05394v1)

Abstract: We present an AI-assisted Augmented Reality assembly workflow that uses deep learning-based object recognition to identify different assembly components and display step-by-step instructions. For each assembly step, the system displays a bounding box around the corresponding components in the physical space, and where the component should be placed. By connecting assembly instructions with the real-time location of relevant components, the system eliminates the need for manual searching, sorting, or labeling of different components before each assembly. To demonstrate the feasibility of using object recognition for AR-assisted assembly, we highlight a case study involving the assembly of LEGO sculptures.

• The paper introduces an AR-assisted framework that integrates deep learning-based YOLOv5 object detection with spatially registered digital instructions.
• The methodology employs synthetic data and homography-based transformations to achieve real-time, robust tracking for complex LEGO assembly tasks.
• The approach eliminates manual part verification and paves the way for interactive, AI-driven assembly workflows adaptable to generative design applications.

AI-Assisted Augmented Reality Assembly: Object Recognition and Computer Vision Applied to Physical Fabrication Workflows

Introduction

This paper details an end-to-end framework for supporting physical assembly tasks using Augmented Reality (AR) technology driven by deep-learning-based object recognition. The system overlays stepwise, spatially contextualized digital instructions onto the physical workspace, dynamically tracking and guiding the user through each assembly phase. The technical approach is instantiated via the Microsoft Hololens 2 platform, combined with a YOLOv5-trained model for detection and localization of discrete components—validated through the assembly of complex LEGO-based sculptures.

System Architecture and Object Recognition Pipeline

The core methodology leverages synthetic data generation for robust model training. Yellow LEGO primitives are rendered under variable lighting and pose, facilitating high variability in training input for the YOLOv5 object detection system. Video streams from the Hololens 2 are decomposed into frames and processed server-side, with YOLOv5 returning 2D bounding boxes for all instances. These 2D projections are then mapped into the AR coordinate system via homography-based planar transformation leveraging the camera pose, thus enabling the system to highlight, in real space, both the current and target locations of components. This configuration delivers high frame-rate detection, crucial for interactive tracking and guidance.

The software stack comprises real-time video streaming, frame-level inference, and synchronized feedback to the AR overlay interface. The bounding box annotations are dynamically generated and advanced automatically in response to assembly state detection, obviating the need for manual progress confirmation or external verification.

Stepwise Instruction Synchronization and Spatial Contextualization

For each assembly instruction, the system highlights only those components and regions relevant to the active task step, minimizing extraneous visual information and reducing cognitive load. The digital bounding boxes indicate both the current position of the component in the workspace and its intended placement in the evolving assembly. Component type labels assist with unambiguous identification, particularly in contexts with visually similar or interchangeable parts. The system visualizes the geometry of the active assembly layer, supporting complex, multi-layer fabrications without occluding or confusing prior steps.

A domain-specific UX strategy places components and the assembly area in distinct spatial regions of the workspace, further reinforcing the separation between selection and placement operations.

Demonstration Results and Assembly Task Validation

The system’s efficacy is demonstrated in the construction of two distinct LEGO sculptures—a twisted wall and an ellipsoidal egg—without recourse to conventional 2D drawing sets or static digital models. The robustness of the object recognition pipeline, coupled with homography-based AR visualization, permits seamless execution of complex multi-step assemblies in a tabletop environment.

No quantitative analysis or cross-comparative benchmarking are reported in this paper; however, the qualitative outcomes indicate complete elimination of manual searching, sorting, or pre-labeling of parts. Further controlled user studies are planned to evaluate accuracy, speed, and reduction of assembly errors.

Implementation Considerations and Scalability

Performance Constraints

• YOLOv5 Inference Latency: With server-side inference, response time is bounded by frame processing time (typically sub-100ms per frame for modest component counts).
• Hololens 2 Camera Quality: The accuracy of spatial localization is dependent on AR headset camera resolution and pose estimation.
• Synthetic Data vs. Real Data Generalization: Performance in cases of occlusion, lighting changes, and non-standard parts may require continual fine-tuning and domain adaptation.

System Robustness

• Occlusion Handling: The use of synthetic datasets increases tolerance to partial occlusions, but tracking reliability for highly cluttered scenes or translucent parts is not fully characterized.
• Layered Assembly Complexity: Since only the active layer is visualized, the approach is well-suited for assemblies following stratified build-up (e.g., LEGO, brickwork, layered parametric kits). Arbitrary, non-layered assemblies may require enhanced registration protocols.

Resource Requirements

• GPU-accelerated inference is required for interactive frame rates with large component sets.
• Server-client architecture imposes networking overhead; reliability increases with on-device inference at the cost of Hololens computational resources.

Theoretical and Practical Implications

By tightly coupling real-time 2D/3D localization with dynamic digital guidance, the system advances AR-assisted physical assembly beyond static overlays to context-aware, state-sensitive interaction. This enables procedural instruction delivery that can adapt to user actions, real-time environment changes, and error correction. Crucially, this workflow suggests a pathway for AI-driven fabrication protocols that integrate with generative design outputs, where AI-generated part geometries are supplied and assembled with minimal human mediation.

Theoretically, this approach bridges spatially registered object detection with semantic task guidance in AR, supporting human-in-the-loop assembly processes and laying foundational infrastructure for future closed-loop fabrication workflows where assembly state can inform, modify, or regenerate digital instructions.

Future Directions

Possible extensions include:

• Multi-object tracking with more complex geometries and a larger set of primitive types
• Direct integration with generative AI for on-demand assembly of algorithmically designed structures
• Automated state recognition for error detection and recovery in assembly tasks
• Quantitative validation across diverse user populations and assembly scenarios
• Application to non-tabletop, large-scale construction and complex robotics-enabled assemblies

Conclusion

This work presents a technically rigorous pipeline for AR-assisted assembly tasks utilizing deep-learning-powered object recognition and spatially synchronized digital instruction overlays. The methodology is validated through complex multi-step LEGO fabrications within an AR-guided interactive environment. The implications for AI-assisted digital fabrication and human-computer interaction are substantial, with direct applicability to generative design workflows and scalable extension to more challenging assembly tasks. Controlled user studies and broader case applications will further contextualize the practical impact of this framework on real-world assembly and fabrication processes.