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
InstTeacher3D0.473 10.778 30.635 10.331 40.322 20.732 10.089 10.502 10.200 10.350 10.440 10.507 10.489 10.493 30.327 10.655 10.469 10.924 10.268 2
WS3D_LR_Ins0.440 20.704 40.533 20.485 10.335 10.659 20.025 50.460 20.156 20.315 20.412 20.254 20.383 20.638 20.278 20.653 20.431 20.869 20.330 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+CSC0.342 30.796 20.412 60.233 60.170 30.637 30.028 20.292 40.089 30.214 30.320 30.133 30.337 40.444 40.242 30.458 50.328 60.852 30.179 3
Ruihang Chu: TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation. CVPR 2022
CSC_LR_INS0.322 40.824 10.433 40.288 50.169 40.610 50.028 20.302 30.084 40.174 50.274 40.061 50.370 30.358 50.142 50.507 40.367 30.701 60.097 6
PointContrast_LR_INS0.304 50.233 60.418 50.389 20.136 60.596 60.006 60.274 50.038 60.140 60.265 50.052 60.314 50.647 10.149 40.533 30.350 50.806 40.132 4
Scratch_LR_INS0.287 60.485 50.438 30.362 30.154 50.614 40.028 20.218 60.058 50.191 40.247 60.065 40.272 60.271 60.109 60.433 60.355 40.766 50.102 5