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 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LR_Ins0.565 10.667 10.755 10.614 10.488 10.767 10.024 20.710 10.392 10.370 10.496 10.298 10.515 10.857 10.496 10.667 10.596 10.990 10.474 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+CSC0.186 20.000 20.545 20.153 20.148 20.667 30.050 10.420 20.007 40.210 30.175 20.016 50.218 20.000 20.000 50.511 20.227 20.000 20.000 5
Ruihang Chu: TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation. CVPR 2022
InstTeacher3D0.162 30.000 20.500 40.000 50.120 30.738 20.000 30.404 30.005 50.291 20.155 30.153 20.008 60.000 20.059 20.289 30.192 40.000 20.000 4
PointContrast_LR_INS0.119 40.000 20.534 30.041 30.047 40.560 40.000 30.307 40.000 60.083 60.071 50.068 40.064 30.000 20.000 50.186 60.176 50.000 20.000 5
CSC_LR_INS0.117 50.000 20.492 50.040 40.032 60.524 50.000 30.174 50.021 20.139 40.096 40.112 30.039 40.000 20.018 30.258 50.150 60.000 20.001 3
Scratch_LR_INS0.101 60.000 20.488 60.000 50.035 50.491 60.000 30.160 60.009 30.111 50.025 60.002 60.009 50.000 20.001 40.265 40.219 30.000 20.001 2