3D Bin Picking AI

Add a real-time perception layer to every bin with vision AI for 3D bin picking. Built for the cells where parts arrive as a random pile instead of a presented row, the depth camera goes blind exactly where the parts are shiniest, and the difference between a running cell and a faulted one is whether the robot can tell where one part ends and the next begins. Whether you're tending machines, kitting, or depalletizing, Roboflow finds and segments every part in the bin, with per-pick records behind every cell.

Detection and Segmentation:

• Detect and segment every part instance in the pile, overlapping, occluded, and at every orientation
• Hold recognition on shiny and dark parts where depth data drops out, so the image sees what the point cloud can't
• Identify SKUs in mixed bins, so the right part comes out of the right pile every time

Picking and Cells:

• Feed masks, positions, and orientation cues to your grasp planner, topmost and least-entangled first
• Verify the grasp took and the place landed, and flag empty bins and foreign objects before the robot dives in
• Run the same perception across machine tending, kitting, depalletizing, and part feeding cells

Operations and Systems Integration:

• Train new parts in days, not feeder-retooling weeks, so high-mix cells stay high-mix
• Track pick rates, misses, and faults by part and cell, so the SKU that fights the gripper surfaces in data
• Integrate with robot controllers from FANUC, ABB, KUKA, and Universal Robots, plus PLCs, WMS, and MES

Bring intelligence to every bin today. Stop perception failures from becoming faulted cells, starving machines, or the automation project that died at the bin.

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More About 3D Bin Picking

What is 3D bin picking with Vision AI?

3D bin picking with vision AI uses computer vision models to give robots the perception a random bin demands: detecting and segmenting every part instance in a pile, separating overlapping and occluded parts, identifying SKUs in mixed bins, feeding position and orientation cues to the grasp planner, and verifying grasps and places. Models trained on your actual parts run at cell speed on edge hardware, with per-pick records behind every cell.

Can Vision AI find parts in a random pile?

The pile is the perception hard case: identical parts overlapping at every angle, occlusion hiding half of most of them, and shiny machined surfaces that punch holes straight through a depth camera's point cloud. Deep-learning segmentation models trained on your actual parts learn what a part looks like from every side and partially hidden, separate each instance from its neighbors, keep working in the specular regions where depth data drops out, and rank what's pickable, which is the difference between a cell that runs the night shift and one that faults on it.

Does this replace our 3D camera and robot software?

No. Your depth camera keeps measuring geometry, and your robot controller and grasp planner keep owning motion, paths, and the gripper. Vision AI adds the recognition layer between them: finding and segmenting each part the point cloud alone can't separate, holding identity through occlusion and glare, flagging empty bins and foreign objects, and handing the planner clean masks and poses to work with. Camera geometry, model perception, and robot execution land in the same per-pick record.

Can it integrate with our robots, PLCs, and WMS?

Yes. Roboflow Inference exposes a standard API and supports common industrial protocols, so picking events flow into your existing systems: robot controllers from FANUC, ABB, KUKA, and Universal Robots, cell PLCs from Allen-Bradley and Siemens, WMS platforms, and MES and ERP platforms like SAP and Oracle, through REST, MQTT, OPC UA, and direct database writes. PLC-level integration coordinates the cell in real time, and every event carries part, bin, cell, imagery, and result, with a full record behind every shift.

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