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 apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LR_Ins0.347 10.593 10.448 10.322 10.224 10.610 20.004 20.488 10.144 10.187 10.311 10.121 10.342 10.424 10.198 10.473 10.333 10.773 10.249 1
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
InstTeacher3D0.117 20.000 20.368 20.000 50.050 30.616 10.000 30.314 20.001 50.187 20.111 20.116 20.007 50.000 20.016 20.216 30.100 40.000 20.000 4
TWIST+CSC0.108 30.000 20.237 30.027 20.055 20.531 30.011 10.290 30.001 40.103 30.099 30.011 50.113 20.000 20.000 50.322 20.143 20.000 20.000 5
Ruihang Chu: TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation. CVPR 2022
PointContrast_LR_INS0.057 40.000 20.184 40.010 30.014 40.400 40.000 30.127 40.000 60.028 60.038 50.021 40.010 30.000 20.000 50.108 60.085 50.000 20.000 5
CSC_LR_INS0.056 50.000 20.177 50.006 40.009 60.370 50.000 30.070 60.003 20.059 40.054 40.052 30.010 40.000 20.004 30.122 50.072 60.000 20.000 3
Scratch_LR_INS0.054 60.000 20.153 60.000 50.011 50.350 60.000 30.108 50.001 30.042 50.010 60.001 60.001 60.000 20.000 40.153 40.141 30.000 20.000 2