Job Description:
We are looking for a highly skilled Computer Vision Engineer to help build the core engine of a construction management SaaS platform. The project has two primary technical pillars:
2D-to-3D Reconstruction: Developing a pipeline to take 2D architectural blueprints (PDF/images), extract structural geometry (walls, openings, dimensions), and convert them into a structured 3D model.
Real-Time Site Monitoring: Implementing real-time object detection using YOLOv8/v10 or Detectron2 to identify construction elements (pipes, windows, doors, ceilings) from site-uploaded photos to track project progress.
Key Responsibilities:
Develop algorithms to parse and binarize 2D blueprints, identifying room boundaries and structural symbols.
Implement geometry extraction logic to transform 2D coordinates into 3D mesh or CAD-compatible data.
Train and fine-tune object detection models (YOLO/Detectron2) on construction-specific datasets.
Integrate model inference into a scalable API (FastAPI/Flask) for our web-based platform.
Optimize models for real-time performance and accuracy in messy, real-world construction site conditions.
Technical Requirements:
Deep Learning Frameworks: Expert proficiency in PyTorch or TensorFlow.
Computer Vision: Extensive experience with OpenCV, YOLO (v8/v10/v11), and Detectron2.
Geometry & Graphics: Experience with 3D reconstruction, point clouds, or mesh generation (e.g., using libraries like Open3D or Trimesh).
Data Pipeline: Experience with data labeling tools (CVAT, Roboflow) and handling technical/architectural drawings.
Backend: Proficiency in Python and building high-performance APIs for ML inference.
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