Zivid

INTRODUCING THE ZIVID 2+ R-SERIES 🎉 We are excited to share with you our latest product series. Discover what these ground-breaking 3D+2D cameras can do for your applications: ✅ 3x faster than our previous model ✅ Best-in-class 2D data ✅ Immunity to ambient light ✅ Can handle all reflections ✅ True-to-reality point clouds ➡️ Product page: https://lnkd.in/eXABFuh9 ➡️ And if you missed it, watch the replay of our launch event here: https://lnkd.in/eru4hbmt #3Dvision #robotics #industrialautomation

Kazuto, our Sales Director in Japan, represented Zivid at the JARSIA (Japan Robot System Integrators Association) Exhibition in Tokyo! 🏯 He showcased our latest Zivid 2+ R-series cameras and delivered an insightful presentation on why point cloud quality matters in industrial automation.   Thank you to everyone who visited our booth and attended the presentation!   Want to learn more? Watch our Zivid 2+ R-series launch video now - it's now publicly available👇 https://lnkd.in/dzmy2_nn   #3DVision #Robotics #IndustrialAutomation

Lifting 50-pound bags for 10 hours a day isn’t just exhausting—it leads to injuries, high turnover, and lost productivity. The CMES Robotics AI-powered depalletizing system takes on the heaviest loads, so your team doesn’t have to. ✅ Lifts heavy bags and boxes with precision ✅ Reduces repetitive strain injuries ✅ Keeps workers focused on high-value tasks Better automation means a safer workplace and a more productive team. See how CMES Robotics can help. ⬇️

Blog: 𝑺𝒐𝒍𝒗𝒊𝒏𝒈 𝑫𝒐𝒖𝒃𝒍𝒆 𝑷𝒊𝒄𝒌𝒔 𝒊𝒏 𝑹𝒐𝒃𝒐𝒕𝒊𝒄 𝑷𝒂𝒓𝒄𝒆𝒍 𝑯𝒂𝒏𝒅𝒍𝒊𝒏𝒈 ✅ 🔗 https://lnkd.in/dqbTJuzc Double picks are a major challenge in robotic parcel induction, leading to mis-sorts, system stoppages, and reduced productivity. This issue becomes even more complex with reflective or irregularly shaped parcels. High-fidelity 2D and 3D vision systems are critical to overcoming these challenges. By accurately segmenting and singulating parcels, these systems ensure precise pick pose estimation and reduce the risk of errors. The result? Faster, more accurate parcel handling that keeps operations running smoothly. 🔎 Want to dive deeper into how advanced vision technology addresses double picks? Read the full blog here: https://lnkd.in/dqbTJuzc #Logistics #Robotics #3DVision #ParcelHandling #IndustrialAutomation

Komm zur LogiMAT-Messe vom 11. bis 13. März! 🦾 Gemeinsam mit Mobile Industrial Robots zeigen wir euch, wie Automatisierung in der #Intralogistik funktioniert! 📍 Messe Stuttgart: Halle 8, Stand 8D41 Was du auf unserem Stand erwarten kannst? ✨ Automatisiertes Handling von Paketen und Paletten mit einem MiR1200 Pallet Jack und MiR1350 AMR und dem UR20 + SICK Sensor Intelligence PALLOC ✨ Präzises Lager- und Kommissioniersystem mit KI-Unterstützung Cellgo x Siemens SIMATIC Robot Pick AI x Zivid ✨ Erlebe unsere Programmiersoftware PolyScope X im Einsatz 🎫Du hast noch kein Ticket? Dann sicher dir jetzt dein kostenloses Ticket: https://lnkd.in/eAXZnUnk

𝐑𝐨𝐛𝐨𝐭𝐢𝐜 𝐏𝐚𝐫𝐜𝐞𝐥 𝐈𝐧𝐝𝐮𝐜𝐭𝐢𝐨𝐧: 𝐓𝐡𝐞 𝐭𝐲𝐩𝐢𝐜𝐚𝐥 𝐢𝐭𝐞𝐦𝐬 𝐲𝐨𝐮𝐫 𝐫𝐨𝐛𝐨𝐭 𝐰𝐢𝐥𝐥 𝐞𝐧𝐜𝐨𝐮𝐧𝐭𝐞𝐫 𝐨𝐧 𝐭𝐡𝐞 𝐜𝐨𝐧𝐯𝐞𝐲𝐨𝐫 𝐛𝐞𝐥𝐭 🚚 🔍 Simple cardboard boxes are easy to capture 🔍 Dark, reflective plastic-wrapped items? Much more difficult 🔍 Stacked letters? You’ll need 3D data precision to pick the correct one 🔗𝐖𝐚𝐭𝐜𝐡 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨: https://lnkd.in/dJDSEFGu Parcel induction is no easy task—especially in warehouse environments with changing lighting conditions. Watch as we put the Zivid 2+ MR130 to the test against two competitor cameras for the unique demands of parcel induction. 𝐂𝐮𝐫𝐢𝐨𝐮𝐬 𝐚𝐛𝐨𝐮𝐭 𝐰𝐡𝐢𝐜𝐡 𝐨𝐧𝐞 𝐜𝐨𝐦𝐞𝐬 𝐨𝐮𝐭 𝐨𝐧 𝐭𝐨𝐩? 👀 ⏯️ Watch here: https://lnkd.in/dJDSEFGu 🔗 For more information about parcel induction, visit our website: https://lnkd.in/dS7tpMna #VisionSystems #Robotics #WarehouseAutomation #3DImaging #ParcelInduction #ParcelHandling #Machinevision #3Dvision

Simple Image Stitching!🎮 In keeping with the theme of retro hardware—and taking inspiration from a comment Alexandre Boffi at Kawasaki Robotics had on my last post—I decided to skip a hardware generation and go straight to this wonderful gem. Rather than just posting an image of a point cloud though, I thought I’d do something different: show how easy it is to stitch point clouds together with Zivid cameras. Why Stitch Point Clouds? Image stitching can be incredibly easy when you have a Zivid camera on a robot with a solid hand-eye calibration. However, since I tragically lack a robot in my home lab (one day I hope…), I'll have to make do with another approach—a Zivid calibration board! The key to stitching (or aligning) point clouds is having a reference to transform one point cloud with respect to another. On a robot with a Zivid camera, this is easy: ✅ You know the camera-to-robot transformation (hand-eye calibration). ✅ The robot itself knows where it is in 3D space (robot pose). Since I don’t have a robot (yet!), I’ll be using a calibration board to establish this relationship instead. The Process Here’s how I went about capturing and stitching this classic from 1996: 1️⃣ Capture the First View I use the Zivid 2+ LR110 to capture an image of the scene. Front and center: the console, games, and the most questionably designed controller of all time. Also in view: the Zivid calibration board to ensure proper alignment. (VIEW 1) 2️⃣ Move the Camera, Capture Again Without touching the setup or calibration grid, I reposition the camera to cover blind spots and areas the first capture couldn’t get to. I take another image, again ensuring the calibration grid and objects remain in view. (VIEW 2) 3️⃣ Detect and Stitch the Point Clouds Using the Zivid Python functions, I extract the calibration grid’s position in both views. This allows me to compute the transformation between the two point clouds. A short bit of Python later, and I transform and concatenate the data to form the final stitched point cloud—now showing the entire scene in beautiful 3D! (VIEW 3) You could add more views if necessary to fill in gaps or missing data, but I left it simple for this. Why Not Use ICP, SURF, or Other Fancy Algorithms? You absolutely can, and in some cases, you should. There are plenty of point cloud registration techniques out there (ICP, SURF, RANSAC, etc.), but thanks to the precision and accuracy of Zivid cameras, this method is so much simpler. No complex feature matching, no iterative alignment—just solid, reliable 3D data. That said… I’m open to feedback! If you have a different take on stitching point clouds, let me know—bonus points if your method is as effective as Oddjob in Slappers Only. 😆 If you’re curious to know more about Zivid cameras, feel free to comment or send me a message. I’d love to share my knowledge and show you the benefits of our technology! #ZividPointCloud #Zivid #3DImaging #3D #Nostalgia #VideoGames #Automation #Vision

Curious about which camera excels in robotic parcel induction? 📦 Find out here: https://bit.ly/3EBOpUM In dynamic warehouse settings, many requirements are crucial to camera performance: ✔️Fast capture time ✔️2D image quality ✔️3D data quality ✔️Ambient light resilience Our expert engineer Christian Aschehoug dives deep into comparing the Zivid 2+ MR130 with two other top contenders on the market... Can you guess which ones? Watch the FULL video to see which camera shines brightest: https://bit.ly/3EBOpUM ➡️ Which feature do you think is most crucial for parcel induction? Share your thoughts! #Warehouse #MachineVision #ParcelInduction #3DVision #Robotics

Did you know that Zivid has a YouTube channel? Our Knowledge Base (https://lnkd.in/gMGviNQw) is fantastic, and covers everything you need to know about using our cameras. From setup, samples, and calculators, all the way to advanced imaging techniques. However, we know that some engineers prefer visual explanations—that’s where our YouTube tutorials come in. If you like learning by seeing things in action, check out our channel for step-by-step guides and best practices. Let us know if there’s a topic you’d like us to cover! https://lnkd.in/gnXF2Ue3 #Zivid #Automation #Tutorials #Vision #3D #2D #PointClouds

I can say without a doubt that these games right here were what sparked my interest in the world of tech. I remember playing them as a kid and wondering, How do these work? What makes this possible? Eventually, curiosity got the best of me. I took one apart (after battling those infamous security screws) and was surprised by what I found. That’s it? A few chips, some circuits—how could something so small create entire worlds? That question stuck with me and, in many ways, set me on the path to what I do today. These classics never made it into 3D until I was older, but today, I have the chance to bring them into the third dimension in a different way. Using our new Zivid 2+ R-series cameras, I captured a 3D point cloud of these legendary games—no cartridge blowing required. It’s amazing to see how far technology has come, from simple 2D sprites to advanced 3D imaging and visualization. But no matter how much things evolve, these classics will always have a special place in my heart. Have you ever revisited an old passion in a new way? Let’s talk about it! #ZividPointCloud #Zivid #3DImaging #3D #2D #Sprites #Nostalgia #VideoGames #Automation #Vision