Parameter Group Template

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After the firmware of V4 cameras is upgraded to 2.2.0, parameter group templates suitable for different scenarios or workpieces are added to the camera. After selecting the parameter group that corresponds to your actual situation, you only need to adjust the parameter values slightly to acquire satisfactory data, simplifying the process of parameter adjustment.

This topic introduces the suitable scenario or workpiece of the parameter group templates for each model.

Model Parameter group Application scenario

DEEP-GL

Sack

Suitable for scenarios of fast imaging of stacks of sacks in logistics and warehousing, providing fast, stable, and high quality image data.

Large carton

Suitable for scenarios of fast imaging of stacks of large cartons in logistics and warehousing, providing fast, stable, and high quality image data.

Plastic turnover box

Suitable for scenarios of fast imaging of stacks of plastic turnover boxes in logistics and warehousing, providing fast, stable, and high quality image data.

Reflective object

Suitable for highly reflective surfaces commonly encountered in machining processes, such as polished metal parts and mirror components. It effectively resolves point cloud loss caused by surface reflections, thereby providing more stable recognition results.

LSR L-GL

Reflective object

Suitable for highly reflective surfaces commonly encountered in machining processes, such as polished metal parts and mirror components. It effectively resolves point cloud loss caused by surface reflections, thereby providing more stable recognition results.

Non-reflective metal

Suitable for scenarios of imaging various sizes of non-reflective metal parts in industrial production, providing fast, stable, and high quality image data.

LSR XL-GL

Reflective object

Suitable for highly reflective surfaces commonly encountered in machining processes, such as polished metal parts and mirror components. It effectively resolves point cloud loss caused by surface reflections, thereby providing more stable recognition results.

NANO-GL

Metal welding

Suitable for imaging metal welding parts in 3D vision-guided applications, providing fast, stable, and high quality image data.

Charging port locating

Suitable for new energy scenarios, enabling precise 3D imaging of the charging port area to quickly identify the location of the charging port on the charging base.

NANO ULTRA-GL

Reflective object

Suitable for highly reflective surfaces commonly encountered in machining processes, such as polished metal parts and mirror components. It effectively resolves point cloud loss caused by surface reflections, thereby providing more stable recognition results.

PRO S-GL

Reflective object

Suitable for highly reflective surfaces commonly encountered in machining processes, such as polished metal parts and mirror components. It effectively resolves point cloud loss caused by surface reflections, thereby providing more stable recognition results.

Small workpiece

Suitable for scenarios of imaging various shapes and sizes of small parts in industrial production, providing fast, stable, and high quality image data.

Threaded rod

Suitable for scenarios of imaging various sizes of threaded rods in industrial production, providing fast, stable, and high quality image data.

Translucent object

Suitable for 3D imaging scenarios involving transparent objects such as packaged plastic bottles, glass bottles, medical liquids, and similar items, providing high quality depth data.

PRO M-GL

Reflective object

Suitable for highly reflective surfaces commonly encountered in machining processes, such as polished metal parts and mirror components. It effectively resolves point cloud loss caused by surface reflections, thereby providing more stable recognition results.

Translucent objects

Suitable for 3D imaging scenarios involving transparent objects such as packaged plastic bottles, glass bottles, medical liquids, and similar items, providing high quality depth data.

UHP-140-GL

Automotive frame

Suitable for comprehensive scanning of subframes in the automotive manufacturing field. It accurately captures the geometric shape of the subframe’s surface and key features (such as circular holes, threaded holes, studs, and waist holes), generating high-quality point cloud data.

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