Being able to retrieve similar types of products a user is searching for is an important core technology for Grainger. Capturing and utilizing exact 3D geometry of the product SKUs (including colors and textures) has significant potential in improving search and matching which currently mostly use matching based on textual descriptions and sometimes images (visual search). Availability of large-scale geometry for 3D matching is limited by current 3D scanning technologies which either require expensive 3D sensors and setups or significant scanning times and processing powers compared to the easier route of taking multiview images for matching. This demo explores a solution to obtain pixel level accurate 3D scans of small texture less objects using single camera setup while reducing scan times and processing costs by combining concepts of projective geometry with recent advances in neural network based semantic segmentation techniques.