Lin L.-HWen H.-KKao M.-HChen ELin T.-HMING OUHYOUNG2021-09-022021-09-022020https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100004631&doi=10.1145%2f3415264.3425437&partnerID=40&md5=73e324fde4bf00377b256614c21a77aehttps://scholars.lib.ntu.edu.tw/handle/123456789/581059We developed an annotation tool - Label360 to solve the distortion and instance matching issues across different viewing aspects in spherical image annotations. A post-processing algorithm was introduced to generate distortion-free annotations on equirectangular images. Two experiments were conducted to examine the consistency of annotations using Label360 and to compare labeling efficiency with LabelMe. Our tool obtained a mean intersection over union (mIoU) of 0.92 in the consistency test and has 1.45x the annotation speed of LabelMe. This demonstrates that Label360 is efficient for annotating instance-aware semantic segmentation labels on spherical images. ? 2020 Owner/Author.Image segmentation; Interactive computer graphics; Semantics; Annotation tool; Consistency tests; Distortion-free; Instance matching; Labeling efficiencies; Postprocessing algorithms; Semantic segmentation; Spherical images; Image annotationconference paper10.1145/3415264.34254372-s2.0-85100004631