Einstar hágæða 3D skanni
Alert me if this item is restocked.
We often restock popular items or they may be returned by the other customers. If you would like to be notified when this happens just enter your details below.
Einstar hágæða 3D skanni
We respect your privacy and don't share your email with anybody.
Alert me if this item is restocked
We have cancelled your request.
Einstar er hágæða 3D skanni á viðráðanlegu verði.
EINSTAR, an affordable handheld 3D scanner, makes high-quality 3D data available to everyone.
In the standard package you will find a handheld 3D scanner with Exstar software, and a lightweight carrying case which includes a series of accessories: a silicone case, introductory user guide, positioning markers, cloth to wipe the lens, a carrying strap, power supply and USB cable.
Variety Applications
Support multiple data format outputs, compatible with 3D printers and 3D design softwares.
High Quality Data
Collect high density point cloud data fast and easily with point distance up to 0.1mm.
High Color Fidelity
Benefit from built-in RGB color camera. 3D data with authentic colors.
Detail-oriented Enhancement Technology
Built-in detail enhancement technology optimizes the point cloud for complete data acquisition.
Streamlined User Experience
Smooth and fast, scanning speed up to 14 FPS.
- · Smart tracking performance
- · Automatic alignment
- · Intelligent algorithm
Stable Outdoor Scanning
Equipped with 3 Infrared VCSEL Projectors, 2 Stereo Depth Cameras and 1 RGB Camera, Einstar can capture clear scan data and generate stable outputs outdoors. Provide a flexible working distance for small and large objects.
Extremely Easy to Use
- · Ergonomic design
- · Only one USB cable for data transmission and power
- · Clear and elegant workflow for scanning and post processing
- · Easy to setup, plug and play
Comfortable for Eyes, Easy to Scan Hair
- · No projector light during scanning process.
- · Comfortable and safe for eyes.
- · Turning on "Hair Mode" to get sufficient hair data for human scan and improve the whole data completeness.
