Robust Background Removal for Object Reconstruction
Deep learning segmentation model for isolating objects from cluttered backgrounds.
Overview
Developed a specialized AI segmentation system to cleanly isolate objects of interest from complex backgrounds, significantly improving the quality of downstream 3D reconstruction tasks.
Challenge
Cluttered backgrounds introduce noise and artifacts in reconstruction. Traditional methods struggle with complex scenes, transparent objects, and fine details like hair or thin structures.
Solution
Trained a custom segmentation AI model using a combination of real and synthetic data. Implemented multi-scale feature extraction and boundary refinement modules to capture fine details. Model optimized for real-time inference.
Impact
Improved reconstruction quality by eliminating background noise. Reduced post-processing time. System now integral part of production reconstruction pipeline.
Key Highlights
- Improved reconstruction quality and robustness
- Removed background noise prior to photogrammetry stages
- Real-time processing at 30 FPS on GPU