2D Semantic instance benchmark
The 2D semantic instance prediction task involves detecting and segmenting the object in an image.
Our evaluation ranks all methods according to the average precision for each class. We report the mean average precision AP (from overlaps [0.5:0.95:0.05]), as well as AP 50% for an overlap value of 50. Note that multiple predictions of the same ground truth instance are penalized as false positives.
This table lists the benchmark results for the 2D semantic instance scenario.
|Method||Info||avg ap||bathtub||bed||bookshelf||cabinet||chair||counter||curtain||desk||door||otherfurniture||picture||refrigerator||shower curtain||sink||sofa||table||toilet||window|
|MaskRCNN_ScanNet||0.119 1||0.129 1||0.212 1||0.002 1||0.112 1||0.148 1||0.014 1||0.205 1||0.044 1||0.066 1||0.078 1||0.095 1||0.142 1||0.030 1||0.128 1||0.139 1||0.080 1||0.459 1||0.057 1|
|Re-implementation of Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick: Mask R-CNN. ICCV'17|