LIDAR stands for LIght Detection and Ranging. It is a remote sensing technology emitting light that travels around the object and back to the receiver creating points every time it hits the object building a 3D map of the entire scene. We help annotate or label cars, pedestrians, bicyclists, trees, animals, traffic lights, billboards, garbage bins, etc in this map by drawing bounding boxes or cuboids precisely to train the machine learning algorithms to interpret the world.
How does LiDAR Annotation work?
LIDAR Annotation: Identifies objects in a 3D point cloud and draws bounding cuboids around the specified objects, returning the positions and sizes of these boxes.
LiDAR annotation is similar to image labeling apart from the difference in practice for a simple reason: It is a 3D representation on a flat-screen. In addition, humans have to deal with a huge amount of points that are not contained by particular boundaries. So, even for professional humans, it is not easy to understand which point belongs to which object, and if you zoom into the point cloud image, this difficulty becomes clear.
Major Role of LiDAR annotation
1) Autonomous vehicles
LiDAR annotation technology is helping ML algorithms mainly by making semantic and instance segmentation of long sequences of LiDAR data highly efficient and accurate. Now it is possible to segment those long sequences in minimal time and with exceptional results.
Data Annotation’s most important type i.e. Lidar annotation INTEGRATED WITH MACHINE LEARNING + Data Annotation and Data Labeling is helping many ways to bring out efficient and accurate results. LiDAR has undergone major changes over the past years, and the most important thing is it has become increasingly very significant due to its fundamental role in autonomous vehicles to safely navigate our roads.
Semantic Segmentation a classic Computer Vision problem that involves taking as input some raw data (2D, 3D images) and label the regions of interest highlighted. Under Semantic Segmentation, we do the process of clustering various parts of images together belonging to the same object class. Leverage our fully-managed human-powered pixel-level image segmentation and annotation to build pixel-perfect semantic segmentation tasks at scale.
This is the most trending technology we adopt to give your self-driven car a better level of accuracy. With Semantic Segmentation we can classify all the pixels of an image into meaningful classes of objects. These classes are “semantically interpretable” and correspond to real-world categories. Learning Spiral provides you the best experience in this field of LiDAR to develop algorithms for your autonomous vehicle.
Semantic segmentation of 3D LiDAR data in dynamic scenes for autonomous driving applications. A system of semantic segmentation using 3D LiDAR data, including range image segmentation, sample generation, inter-frame data association, track-level annotation, and semi-supervised learning, is developed. The qualitative and quantitative experiments show that the combination of a few annotations and a large amount of constraint data significantly enhances the effectiveness and scene adaptability, resulting in greater than 10% improvement.
About the Organization
Learning Spiral, Data Labeling company provides data annotation and data labeling services including Lidar annotation is here to Empower your algorithm with our human data labeling. Our ISO 27000 certified facilities are equipped to handle the most secure data, and our training data expertise helps reduce ramp time and increase quality.
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