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 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_Inspermissive0.579 10.667 20.758 10.655 30.498 10.750 10.042 40.779 10.163 20.429 10.472 20.408 10.625 11.000 10.469 30.680 30.612 11.000 10.404 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_LA0.565 21.000 10.719 40.649 40.407 20.742 20.135 20.598 20.298 10.368 20.509 10.343 20.503 20.981 30.347 40.755 10.521 20.983 40.315 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_INS0.460 30.528 40.754 30.709 10.314 50.542 40.139 10.472 40.120 30.302 40.373 30.090 40.389 40.714 40.631 10.531 50.467 31.000 10.203 4
PointContrast_LA_INS0.456 40.667 20.696 50.687 20.325 40.543 30.072 30.556 30.095 40.270 50.315 40.021 50.318 51.000 10.562 20.585 40.319 50.994 30.188 5
Scratch_LA_INS0.418 50.528 40.758 20.475 50.377 30.484 50.008 50.450 50.029 50.311 30.260 50.096 30.500 30.714 40.309 50.681 20.370 40.938 50.231 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_ODpermissive0.344 10.667 10.816 10.593 10.084 10.640 10.054 10.059 20.400 10.166 10.228 10.027 10.317 10.201 10.155 10.735 10.272 10.674 10.100 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_DET0.182 20.667 10.343 40.262 30.016 20.414 30.000 30.098 10.159 20.069 30.044 30.001 20.159 30.072 30.026 30.527 20.072 30.326 40.024 2
PointContrast_LA_DET0.162 30.167 40.503 20.273 20.004 40.446 20.000 20.030 30.077 40.078 20.049 20.000 30.231 20.113 20.013 40.501 30.075 20.346 30.016 3
Scratch_LA_DET0.148 40.667 10.389 30.083 40.005 30.324 40.000 30.004 40.078 30.041 40.016 40.000 30.124 40.003 40.037 20.443 40.063 40.381 20.006 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 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_Ins0.656 11.000 10.766 40.736 10.656 10.793 30.091 20.561 20.389 40.533 10.582 10.531 20.592 10.981 10.622 10.782 20.638 20.997 40.558 1
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
InstTeacher3D0.646 21.000 10.764 50.699 30.563 20.849 10.177 10.694 10.449 10.509 20.561 20.575 10.550 20.714 30.595 20.777 40.650 10.997 40.513 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
TWIST+CSC0.550 31.000 10.758 60.570 60.462 30.797 20.050 30.389 50.436 20.433 30.484 30.253 30.491 40.571 40.538 30.760 50.561 51.000 10.354 3
Ruihang Chu: TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation. CVPR 2022
CSC_LR_INS0.529 41.000 10.773 10.704 20.414 40.786 40.050 30.412 40.394 30.376 50.442 40.179 40.542 30.539 50.394 50.793 10.564 40.944 60.217 6
PointContrast_LR_INS0.488 50.472 60.773 10.594 50.374 60.774 60.013 60.353 60.252 60.327 60.416 50.087 60.435 60.857 20.444 40.779 30.560 61.000 10.282 4
Scratch_LR_INS0.473 60.528 50.773 10.632 40.391 50.784 50.050 30.515 30.271 50.392 40.400 60.123 50.486 50.387 60.322 60.627 60.573 31.000 10.268 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