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Label360: An Implementation of a 360 Segmentation Labelling Tool | Taiwan AILabs
The image above shows an example of the segmentation mask overlaying on top of the 360 image we got from our drone. This image is labeled by one of our in-house labelers. Semantic segmentation is one of the key problems in computer vision. It is important for image analysis tasks, and it paves the way towards scene understanding. Semantic segmentation refers to the process of assigning each pixel of the image with a class label, such as sky, road, or person. There are numerous applications that nourish from inferring knowledge from imagery. Some applications include self-driving vehicles, human-computer interaction, and virtual reality. 360 images and videos are popular nowadays for applications like game design, surveillance systems, and virtual tourism. Researchers use 360 images as input for object detection and semantic segmentation models. However, researchers usually convert 360 images to normal field-of-view first before labelling them. For example, Stanford 2D-3D