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_Ins0.426 10.741 10.580 10.409 10.318 10.665 20.011 30.512 10.143 10.269 20.370 20.293 20.359 20.656 10.204 20.601 10.401 20.830 20.302 1
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
InstTeacher3D0.416 20.741 10.525 20.132 50.230 20.707 10.119 10.251 40.098 20.297 10.398 10.461 10.440 10.616 20.319 10.567 20.436 10.921 10.238 2
TWIST+CSC0.295 30.537 30.396 50.148 40.140 30.625 30.003 40.439 20.023 60.159 30.251 30.166 30.228 60.444 30.193 30.435 40.324 30.689 60.117 3
Ruihang Chu: TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation. CVPR 2022
PointContrast_LR_INS0.264 40.472 60.423 30.170 20.110 40.575 60.001 60.344 30.030 50.127 40.232 50.065 60.351 30.250 50.087 50.478 30.253 40.722 30.068 4
CSC_LR_INS0.259 50.537 30.310 60.126 60.077 60.617 40.020 20.178 60.050 30.111 60.251 40.136 40.319 40.387 40.146 40.406 50.212 60.714 40.058 5
Scratch_LR_INS0.241 60.528 50.399 40.152 30.101 50.578 50.001 50.208 50.035 40.126 50.197 60.093 50.237 50.226 60.069 60.387 60.251 50.711 50.042 6