The 3D semantic instance prediction task involves detecting and segmenting the object in an 3D scan mesh.

Evaluation and metrics

Our evaluation ranks all methods according to the average precision for each class. We report the mean average precision AP at overlap 0.25 (AP 25%), overlap 0.5 (AP 50%), and over overlaps in the range [0.5:0.95:0.05] (AP). Note that multiple predictions of the same ground truth instance are penalized as false positives.



This table lists the benchmark results for the 3D semantic instance with limited reconstructions scenario.




Method Infoavg ap 25%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LR_Ins0.715 11.000 10.889 10.685 10.724 10.835 10.389 10.710 10.754 10.506 10.655 10.373 10.550 10.857 10.731 10.866 10.694 10.990 10.663 1
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
TWIST+CSC0.288 20.000 20.582 20.563 20.322 20.758 30.269 20.475 20.196 30.291 30.223 20.055 50.242 20.000 20.080 40.760 20.374 30.000 20.001 6
Ruihang Chu: TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation. CVPR 2022
CSC_LR_INS0.217 30.000 20.525 50.221 40.114 60.652 50.055 40.214 60.301 20.283 40.156 40.286 20.076 40.000 20.212 20.546 30.257 50.000 20.005 4
PointContrast_LR_INS0.206 40.000 20.573 40.291 30.136 50.697 40.036 60.461 30.108 50.220 60.129 50.079 40.210 30.000 20.029 60.455 50.278 40.000 20.011 2
InstTeacher3D0.203 50.000 20.500 60.000 60.245 30.791 20.114 30.404 40.005 60.379 20.180 30.207 30.008 60.000 20.059 50.534 40.223 60.000 20.001 5
Scratch_LR_INS0.181 60.000 20.576 30.057 50.171 40.621 60.045 50.256 50.186 40.241 50.081 60.006 60.056 50.000 20.147 30.409 60.399 20.000 20.006 3