This table lists the benchmark results for the 3D semantic label with limited annotations scenario.




Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Q2E0.741 10.984 10.821 20.757 40.739 10.868 20.600 10.849 10.595 60.659 10.971 20.490 20.299 20.689 40.822 30.749 10.788 40.641 10.935 20.860 10.699 2
ActiveST0.735 20.983 20.769 40.798 10.701 20.852 50.527 20.801 20.680 10.629 20.973 10.447 100.312 10.757 10.799 40.747 20.795 30.632 20.952 10.855 20.684 3
Gengxin Liu, Oliver van Kaick, Hui Huang, Ruizhen Hu: Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation.
DE-3DLearner LA0.704 30.774 70.766 50.764 30.687 40.832 70.413 110.790 40.639 20.599 40.952 40.478 60.222 80.746 20.859 10.678 40.806 20.607 60.915 50.847 30.703 1
Ping-Chung Yu, Cheng Sun, Min Sun: Data Efficient 3D Learner via Knowledge Transferred from 2D Model. ECCV 2022
WS3D_LA_Sempermissive0.689 40.879 30.753 60.798 10.648 80.816 90.421 100.796 30.604 50.603 30.945 100.457 90.204 90.559 100.851 20.724 30.760 70.630 30.903 70.821 50.603 8
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
VIBUSpermissive0.684 50.848 40.752 70.708 90.691 30.861 30.474 50.770 50.611 40.538 90.951 50.478 60.275 40.676 50.671 110.649 80.788 40.610 50.869 90.808 100.657 4
Beiwen Tian,Liyi Luo,Hao Zhao,Guyue Zhou: VIBUS: Data-efficient 3D Scene Parsing with VIewpoint Bottleneck and Uncertainty-Spectrum Modeling. ISPRS Journal of Photogrammetry and Remote Sensing
GaIA0.682 60.731 110.846 10.713 80.657 60.869 10.475 40.705 90.452 130.569 50.951 50.563 10.290 30.544 110.799 40.677 50.810 10.618 40.900 80.821 50.642 5
Min Seok Lee*, Seok Woo Yang*, and Sung Won Han: GaIA: Graphical Information gain based Attention Network for Weakly Supervised 3D Point Cloud Semantic Segmentation. WACV 2023
LE0.680 70.744 90.731 90.727 60.664 50.859 40.427 90.759 60.562 70.562 60.948 70.480 40.245 60.735 30.765 60.648 100.786 60.591 70.931 30.817 70.624 7
One-Thing-One-Click0.670 80.734 100.815 30.661 130.644 90.841 60.509 30.741 70.479 120.548 70.968 30.461 80.251 50.664 60.754 70.656 70.744 100.541 110.917 40.844 40.625 6
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Viewpoint_BN_LA_AIR0.650 90.778 60.731 90.688 110.617 110.812 110.446 70.739 80.618 30.540 80.945 100.415 110.204 90.623 70.676 100.594 110.744 100.576 80.868 100.811 80.582 10
Liyi Luo, Beiwen Tian, Hao Zhao, Guyue Zhou: Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck.
CSC_LA_SEM0.644 100.761 80.707 120.703 100.642 100.813 100.436 80.659 110.502 90.516 110.945 100.487 30.238 70.538 120.678 90.659 60.739 120.568 100.915 50.811 80.566 12
PointContrast_LA_SEM0.636 110.694 120.738 80.731 50.653 70.817 80.467 60.651 120.517 80.522 100.946 80.479 50.198 110.575 90.526 130.649 80.747 80.569 90.845 110.803 110.600 9
Scratch_LA_SEM0.621 120.802 50.715 110.687 120.570 120.800 120.386 120.703 100.486 110.514 120.946 80.390 120.181 120.620 80.670 120.487 130.746 90.539 120.804 120.798 120.580 11
SQN_LA0.576 130.674 130.670 130.722 70.454 130.790 130.342 130.622 130.487 100.427 130.933 130.357 130.157 130.452 130.721 80.492 120.696 130.487 130.790 130.748 130.507 13


This table lists the benchmark results for the 3D semantic instance with limited annotations 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_LA_Inspermissive0.364 10.556 20.444 10.328 20.222 10.598 10.005 40.544 10.065 10.185 10.274 20.177 20.452 10.654 10.171 30.455 20.333 10.892 10.193 1
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
Box2Mask_LA0.327 20.852 10.336 50.381 10.162 20.508 20.052 10.205 50.061 20.154 20.313 10.208 10.299 30.530 30.165 40.492 10.213 30.784 20.162 2
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
CSC_LA_INS0.258 30.485 40.356 40.298 30.114 40.364 30.044 20.223 40.022 30.136 30.217 30.036 30.214 40.507 40.191 20.363 40.221 20.776 30.079 4
PointContrast_LA_INS0.256 40.519 30.376 30.216 50.099 50.347 40.026 30.443 20.017 40.113 50.142 40.008 50.137 50.586 20.219 10.362 50.149 50.775 40.078 5
Scratch_LA_INS0.241 50.463 50.402 20.220 40.131 30.306 50.005 40.307 30.006 50.131 40.131 50.034 40.301 20.376 50.114 50.427 30.186 40.723 50.083 3


This table lists the benchmark results for the 3D object detection with limited annotations 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_LA_ODpermissive0.366 11.000 10.825 10.698 10.068 10.665 10.007 40.210 10.409 10.173 10.189 10.019 10.375 10.062 40.176 10.730 10.227 10.587 20.162 1
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
CSC_LA_DET0.274 20.667 20.458 40.189 20.034 30.528 20.093 10.098 30.329 20.117 30.147 20.004 20.318 20.154 20.136 20.687 20.166 20.733 10.072 3
PointContrast_LA_DET0.246 30.667 20.587 20.173 30.032 40.525 30.018 30.011 40.199 40.157 20.121 30.003 30.253 30.191 10.100 30.570 40.156 30.586 30.073 2
Scratch_LA_DET0.217 40.667 20.575 30.139 40.035 20.386 40.064 20.121 20.256 30.073 40.064 40.000 40.112 40.152 30.032 40.595 30.124 40.492 40.020 4


This table lists the benchmark results for the 3D semantic label with limited reconstructions scenario.




Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LR_Sem0.682 10.864 20.766 40.785 10.645 10.805 70.443 20.793 10.600 10.588 10.944 70.457 10.222 10.537 30.773 20.724 10.755 20.622 10.907 20.821 30.597 3
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
NWSYY0.670 20.745 40.784 20.780 30.611 30.829 50.463 10.692 20.585 30.542 30.948 20.448 20.219 20.554 20.828 10.702 20.758 10.573 30.844 70.825 20.674 1
DE-3DLearner LR0.663 30.851 30.770 30.760 40.615 20.830 40.439 30.670 30.546 50.587 20.955 10.406 40.177 40.627 10.758 30.606 40.740 30.549 40.888 30.840 10.652 2
Ping-Chung Yu, Cheng Sun, Min Sun: Data Efficient 3D Learner via Knowledge Transferred from 2D Model. ECCV 2022
Viewpoint_BN_LR_AIR0.625 40.873 10.727 60.709 50.535 70.820 60.402 60.643 50.540 60.501 60.946 40.352 80.181 30.535 40.594 70.596 50.685 80.543 50.927 10.792 40.592 4
CSC_LR_SEM0.612 50.739 70.794 10.687 80.564 40.850 10.347 80.590 70.587 20.521 40.945 60.358 70.140 60.522 60.496 80.627 30.725 50.598 20.850 40.792 40.508 5
PointContrast_LR_SEM0.603 60.740 60.700 80.700 60.546 60.843 20.419 40.592 60.462 70.513 50.946 40.374 50.104 80.530 50.687 40.571 80.694 70.519 70.850 40.781 70.484 8
Scratch_LR_SEM0.596 70.745 40.722 70.783 20.486 80.834 30.414 50.667 40.398 80.492 70.948 20.359 60.124 70.406 80.636 50.574 60.708 60.503 80.849 60.784 60.490 7
CSG_3DSegNet0.594 80.732 80.755 50.697 70.548 50.790 80.363 70.432 80.576 40.448 80.933 80.414 30.168 50.486 70.621 60.574 60.727 40.539 60.807 80.779 80.498 6


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
Yizheng Wu, Zhiyu Pan, Kewei Wang, Xingyi Li, Jiahao Cui, Liwen Xiao, Guosheng Lin, Zhiguo Cao: Instance Consistency Regularization for Semi-Supervised 3D Instance Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence
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


This table lists the benchmark results for the 3D object detection 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_ODpermissive0.374 10.867 10.797 10.655 10.104 10.678 10.046 10.215 10.406 10.186 10.219 10.034 10.354 10.160 10.101 10.741 10.306 10.679 10.181 1
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
CSC_LR_DET0.191 20.667 20.468 30.226 20.036 20.420 30.025 20.010 30.081 30.066 30.045 20.000 20.162 30.010 30.017 20.657 20.109 20.420 30.013 2
PointContrast_LR_DET0.187 30.667 20.523 20.109 30.027 30.435 20.005 30.013 20.199 20.070 20.035 30.000 40.183 20.033 20.003 40.497 30.078 30.488 20.005 3
Scratch_LR_DET0.076 40.667 20.099 40.015 40.005 40.190 40.000 40.000 40.033 40.007 40.001 40.000 30.000 40.010 40.004 30.094 40.014 40.237 40.000 4