Mindkosh AI

Point cloud object detection has transformed the way machines interpret 3D environments. This is particularly helpful for applications like autonomous systems, robotics, and augmented reality. In our latest blog we discuss point cloud object detection, exploring various tradional as well as deep learning based methods, challenges as well as applications. Do check it out and let us know your thoughts in the comment section below. https://lnkd.in/gGAfu5C3 #pointcloud #computervision #objectdetection

Video annotation is changing the landscape of modern surveillance systems, self-driving cars, and content moderation. Our latest blog discusses video annotation and its role in building intelligent systems, exploring key techniques, tools, and real-world applications across industries. We also discuss challenges and future trends shaping this evolving field. This blog is a valuable resource for enthusiasts in computer vision and AI. Do check it out and let us know your thoughts in the comment section below. https://lnkd.in/g9Zk9w48 #videoannotation #computervision #AI

Powerful filtering is not just about providing visual simplicity. It's also about creating workflows where we can zoom in on key areas and understand complex point cloud data better. In the video below you can see the power of filtering points in point clouds. This enables us selectively to eliminate points from specific classes, reducing clutter. It's easier to label and analyze dense, busy areas. Contact us today to see the platform in action! #lidar #pointclouds #machinelearning #deeplearning #adas #autonomy

🗿 Lidar is revolutionizing archaeology by changing the way ancient landscapes are explored. By using laser pulses to create 🗺 high-resolution maps of the ground, archaeologists can now uncover hidden structures deep beneath the surface, discovering lost cities and forgotten civilizations that would otherwise remain buried under dense vegetation or rugged terrain. In our latest blog, we see how lidar works and how it is re-shaping archaeological discovery. From mapping Mayan ruins to Roman roads, Lidars offer a non-Invasive, efficient, and elaborate way to study our past. Check it out here and let us know your thoughts in the comments section below! 🔗 https://lnkd.in/gUFT7uny #archaelogy #lidar #pointclouds

🗺 Lidar mapping is an emerging new technology that allows the creation of highly detailed 3D maps of the environment. In our latest blog we take a look at how Lidar mapping works, what advantages it offers and which industries have already adopted it for better outcomes. If you work in the fields of GIS (Geographic Information System), or are interested in learning more about Lidars, this post could be an especially interesting read. 🔗Read it here & and let us know your thoughts in the comments below https://lnkd.in/gXMjeKbs #lidar #pointclouds #GIS #mapping #machinelearning #deeplearning

Objects in 3D point clouds often have relatively regular dimensions, while their positions and orientations change constantly. When setting up an ontology on Mindkosh, you can now set default sizes for specific classes. These can then be used to create cuboids of specified default sizes centered around the click point. This can drastically reduce cuboid drawing times, while also ensuring high quality labeling. Get in touch with us to see how Mindkosh can help you create high quality labeled datasets. 💬 #lidar #sensorfusion #multisensordata #autonomy #machinelearning #pointclouds

Lidars are being rapidly adopted across industries. But why? 🤔 Lidars offer highly accurate depth perception, and provide an inherently 3 dimensional view of the environment. While they were traditionally an expensive commodity, 💵 prices have dropped sharply in recent years, making them suitable for a variety of applications. In our latest blog we look at the reasons fueling this adoption, and explore which industries have already started using Lidars to improve their core offerings. Read it here 🔗 https://lnkd.in/gE9uBynf #lidar #machinelearning #deeplearning #pointclouds #autonomousmobility

Lidar, Radar and Sonar offer unique perspectives on the 🌏 world around us. While Lidars provides a high resolution, 3D view of the environment, Radars provide accurate speed and distance measurements of objects over long distances. Sonars on the other hand are more accurate than both of these underwater. With the advent Autonomous mobility, these sensors are finding more and more use cases, specially when combined with camera images through various Sensor Fusion techniques. In our latest blog, we cover the unique advantages as well as disadvantages of all these sensors, while also looking at how Sensor fusion can help provide a holistic view of the environment . Do check it out and let us know your thoughts in the comment section below ! 👉 https://lnkd.in/gt-CqWAu #LIDAR #RADAR #SONAR #ComputerVision #machinelearning #adas #autonomousmobility #autonomousvehicles

🤔 Curious to know more about 3D point cloud segmentation techniques ? Point cloud segmentation is an important task for various applications like 3D perception, surveying etc. In our latest blog we explore various techniques used for point cloud segmentation, including K-means clustering, region growing, Graph-based Techniques and Deep learning based methods like 3D-CNN. Check out the post here and do let us know your thoughts in the comment section below! 👉 https://lnkd.in/gY8uSjRg #3DVision #PointCloudSegmentation #deeplearning #lidar #adas #autonomousmobility

Compared to bounding boxes, polygons can detect objects with more precision. 📈 For example, while bounding boxes can include a lot of the background for rotated objects, polygons can help ML models capture accurate details of the object's location. In our latest blog, we take a look at polygon annotation, covering topics like what advantages it offers, what annotation tools to use, popular ML models in use and more. Click here to read the blog 👉 https://lnkd.in/dkPAC_SN #PolygonAnnotation #MachineLearning #ComputerVision #DataAnnotation