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
ActiveST0.748 10.984 10.804 30.759 50.720 20.849 50.516 20.791 30.670 10.654 20.974 10.495 50.382 10.811 10.828 50.787 10.780 60.640 20.952 10.861 30.701 1
Gengxin Liu, Oliver van Kaick, Hui Huang, Ruizhen Hu: Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation.
Q2E0.743 20.984 10.803 40.770 10.725 10.881 10.572 10.806 20.663 20.665 10.972 20.506 30.305 20.652 60.829 40.761 20.809 20.660 10.951 20.862 20.682 2
DE-3DLearner LA0.709 30.877 40.772 80.744 90.694 30.836 70.453 60.787 40.623 40.598 40.953 40.490 70.216 110.682 50.879 10.727 30.802 30.604 50.922 30.845 40.676 3
Ping-Chung Yu, Cheng Sun, Min Sun: Data Efficient 3D Learner via Knowledge Transferred from 2D Model. ECCV 2022
WS3D_LA_Sempermissive0.694 40.895 30.743 100.767 20.675 60.826 100.496 30.817 10.612 50.613 30.947 100.460 90.254 60.558 110.811 70.710 50.776 80.616 30.874 110.822 60.603 12
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
One-Thing-One-Click0.694 40.760 90.815 20.706 130.684 50.840 60.492 40.701 90.557 70.596 50.972 20.497 40.281 40.709 20.757 80.689 60.789 40.600 70.907 70.864 10.671 4
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
VIBUSpermissive0.691 60.860 50.731 120.738 100.672 70.860 20.470 50.766 50.625 30.547 110.949 50.491 60.255 50.693 40.715 100.712 40.778 70.597 80.911 50.816 90.635 7
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
LE0.688 70.856 70.779 60.754 70.687 40.834 80.438 80.732 70.536 90.577 60.948 60.508 20.248 70.699 30.831 30.636 80.752 110.586 90.895 90.821 70.643 6
GaIA0.685 80.759 100.834 10.759 50.650 80.859 30.427 100.694 100.524 100.575 70.948 60.537 10.304 30.534 120.853 20.678 70.820 10.581 100.914 40.828 50.626 8
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
Viewpoint_BN_LA_AIR0.669 90.847 80.732 110.724 110.613 120.827 90.443 70.742 60.562 60.551 100.947 100.441 120.218 100.650 70.753 90.621 90.765 100.601 60.905 80.814 120.618 9
Liyi Luo, Beiwen Tian, Hao Zhao, Guyue Zhou: Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck.
CSC_LA_SEM0.665 100.857 60.756 90.763 40.647 90.852 40.432 90.684 120.543 80.514 120.948 60.469 80.179 120.599 90.702 110.620 100.789 40.614 40.911 50.815 110.607 11
PointContrast_LA_SEM0.653 110.717 120.775 70.754 70.626 110.804 130.391 120.689 110.485 130.572 90.945 120.448 100.232 90.603 80.813 60.591 120.775 90.537 120.885 100.816 90.608 10
Scratch_LA_SEM0.643 120.699 130.793 50.718 120.636 100.816 110.411 110.707 80.490 120.574 80.948 60.448 100.173 130.559 100.689 120.604 110.722 120.556 110.853 120.820 80.651 5
SQN_LA0.598 130.741 110.681 130.766 30.482 130.805 120.389 130.658 130.499 110.437 130.936 130.386 130.243 80.422 130.663 130.552 130.700 130.519 130.809 130.750 130.515 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_Inspermissive0.442 10.787 10.554 10.488 10.363 10.651 10.035 10.440 10.202 10.307 10.417 10.253 20.329 30.617 10.243 10.594 10.467 10.867 10.348 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_LA0.341 20.667 20.265 50.385 20.200 20.536 20.026 20.314 30.074 20.159 40.402 20.281 10.335 20.450 50.170 30.570 20.241 50.836 30.221 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_INS0.293 30.485 30.397 30.333 30.168 30.415 30.010 30.337 20.018 40.168 30.263 30.057 30.193 50.495 30.141 40.525 40.318 20.837 20.120 5
PointContrast_LA_INS0.278 40.472 40.427 20.229 50.121 50.327 40.009 40.257 40.021 30.180 20.176 50.019 50.350 10.460 40.098 50.563 30.292 30.834 40.163 3
Scratch_LA_INS0.248 50.122 50.376 40.319 40.158 40.319 50.006 50.239 50.013 50.138 50.206 40.029 40.299 40.506 20.183 20.441 50.259 40.738 50.121 4


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_Sem0.684 10.865 10.761 10.780 10.644 10.810 20.445 10.796 10.596 10.594 10.945 20.456 10.234 10.541 10.793 10.723 10.761 10.618 10.906 10.822 10.598 2
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
NWSYY0.517 20.725 30.619 60.396 40.455 30.766 50.327 30.570 20.477 50.427 30.943 30.288 20.220 30.274 50.135 30.471 30.697 30.504 20.714 50.767 30.566 3
DE-3DLearner LR0.508 30.824 20.530 80.314 50.479 20.746 70.334 20.490 40.508 20.477 20.950 10.269 30.221 20.324 30.029 60.421 50.626 60.490 30.727 30.782 20.620 1
Ping-Chung Yu, Cheng Sun, Min Sun: Data Efficient 3D Learner via Knowledge Transferred from 2D Model. ECCV 2022
CSG_3DSegNet0.480 40.521 50.715 30.562 20.389 60.693 80.307 40.157 80.501 30.321 80.927 80.219 60.074 80.329 20.485 20.504 20.596 80.458 50.715 40.714 80.418 4
CSC_LR_SEM0.460 50.472 70.731 20.465 30.398 40.817 10.292 50.442 50.311 80.387 60.939 40.218 70.181 40.302 40.076 40.449 40.743 20.430 70.444 80.737 50.368 7
Viewpoint_BN_LR_AIR0.452 60.587 40.569 70.172 70.391 50.769 40.290 60.512 30.501 30.373 70.935 60.251 40.173 50.201 60.003 80.352 70.619 70.454 60.783 20.719 70.390 6
PointContrast_LR_SEM0.438 70.517 60.659 50.251 60.332 80.783 30.244 80.408 60.411 70.409 40.935 60.206 80.119 70.200 70.048 50.355 60.682 40.414 80.647 60.743 40.391 5
Scratch_LR_SEM0.401 80.240 80.674 40.095 80.347 70.763 60.271 70.204 70.449 60.406 50.936 50.220 50.127 60.199 80.004 70.348 80.665 50.477 40.493 70.730 60.366 8


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
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+CSC0.257 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


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