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Bounding box annotation is the most common and widely used model for Machine learning tasks. The annotators will draw a bounding box over an object and label them with an object class. Generally the bounding boxes are drawn tight with no loose ends. The task seems simple but it requires meticulous effort to keep up the consistency. Bounding boxes are generally used for image annotations.
Infosearch offers 2D & 3D bounding box annotation outsourcing services for computer vision and machine learning applications. The use cases are for autonomous vehicle, SKU classification in retail industry, food tray, House objects, Agrotech and many more.
Polygon is a multi point annotation technique to draw shapes, angles and curves unlike bounding box annotation which limits only to square or rectangle boxes. Polygon helps to annotate objects in angled photos and polygon shapes.
Cuboid annotation is drawing a cube over an object to get 3D perspectives on Height, Width and Depth. The cuboids are drawn on 2D images to get the 3D perspectives. This type of annotation is widely used in road scenes to distinguish cars, trucks, bus, van, pavement, pedestrians etc. However cuboid annotation can be applied to any type of images.
We use tools which can propagate and interpolate sequence images for faster annotation tasks. The propagated images will have cuboids drawn over it and the annotators are required to adjust the box sizes and dimensions.