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_Ins0.358 10.546 40.392 30.384 10.225 10.609 20.008 10.520 10.136 10.196 20.260 20.065 40.338 10.593 10.191 10.547 10.335 10.859 10.244 1
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
InstTeacher3D0.310 20.741 10.407 20.173 20.164 20.675 10.000 40.249 30.061 20.308 10.340 10.336 10.206 20.000 30.160 30.441 20.318 20.821 20.179 2
TWIST+CSC0.257 30.556 20.408 10.073 30.139 30.600 30.001 20.334 20.058 30.169 30.228 30.104 20.200 30.143 20.132 40.404 40.232 30.764 30.085 3
Ruihang Chu: TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation. CVPR 2022
CSC_LR_INS0.196 40.556 20.364 50.026 40.064 40.528 40.000 40.159 50.040 40.126 40.158 40.075 30.039 50.000 30.165 20.417 30.192 50.596 50.025 4
PointContrast_LR_INS0.176 50.444 60.375 40.001 60.057 60.465 60.000 40.234 40.021 60.092 60.137 50.035 60.004 60.000 30.079 50.391 50.204 40.620 40.016 6
Scratch_LR_INS0.156 60.481 50.279 60.023 50.061 50.499 50.001 30.001 60.025 50.114 50.108 60.055 50.047 40.000 30.023 60.325 60.176 60.568 60.018 5