Overview
This project addresses the critical challenge of accurate pose and shape estimation for boxes in large-scale warehouse environments, where traditional methods often struggle with the variety and complexity of real-world scenarios.
Research Challenge
Warehouse environments present unique challenges for computer vision systems:
- High variability in box types and sizes
- Complex lighting conditions
- Occlusion and clutter
- Real-time processing requirements
Key Contributions
Domain Adaptation
- Novel techniques for adapting to warehouse environments
- Handling domain shift between training and testing
- Robust performance across different warehouse layouts
Box Pose and Shape Estimation
- Accurate estimation of 3D pose and dimensions
- Real-time processing for industrial applications
- Robust to various box types and conditions
Large-Scale Validation
- Testing in real warehouse environments
- Performance evaluation on diverse scenarios
- Scalable solutions for industrial deployment
Applications
- Automated warehouse management
- Inventory tracking and management
- Robotic picking and sorting
- Quality control and inspection
Publications
- “Box Pose and Shape Estimation and Domain Adaptation for Large-Scale Warehouse Automation” - ISER 2025
Industry Impact
This work has direct applications in:
- E-commerce fulfillment centers
- Manufacturing logistics
- Distribution networks
- Automated storage systems
Links
