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 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_Inspermissive0.668 11.000 10.786 10.845 10.647 10.777 10.137 10.618 20.432 10.506 10.564 20.561 10.546 31.000 10.562 10.786 20.675 11.000 10.577 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_LA0.592 21.000 10.619 50.820 20.471 20.773 20.104 20.618 10.377 20.409 20.591 10.364 20.515 40.857 20.443 30.782 30.524 41.000 10.382 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_INS0.494 30.528 40.766 30.800 30.408 40.611 30.045 30.547 40.055 50.368 30.429 30.126 30.389 50.819 30.421 40.775 40.550 21.000 10.253 4
PointContrast_LA_INS0.471 40.667 30.773 20.646 50.330 50.490 40.032 40.470 50.122 30.368 40.349 50.048 50.592 10.614 50.338 50.789 10.536 30.997 40.316 3
Scratch_LA_INS0.464 50.222 50.763 40.714 40.464 30.469 50.027 50.591 30.079 40.303 50.395 40.075 40.550 20.777 40.458 20.712 50.512 50.997 40.243 5


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_ODpermissive0.341 11.000 10.629 10.426 10.070 10.608 10.063 10.176 10.503 10.132 10.084 10.001 10.337 10.220 10.103 10.628 10.282 10.739 10.131 1
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
PointContrast_LA_DET0.135 20.667 20.296 30.145 20.002 30.330 30.000 40.000 30.050 30.049 20.023 20.000 30.002 40.006 40.034 30.472 20.052 20.285 40.011 2
CSC_LA_DET0.135 20.444 30.336 20.029 40.001 40.356 20.008 20.000 20.011 40.045 30.010 40.000 20.032 20.011 30.043 20.458 30.028 30.602 20.010 3
Scratch_LA_DET0.098 40.167 40.253 40.074 30.002 20.257 40.004 30.000 40.080 20.038 40.013 30.000 30.006 30.143 20.002 40.243 40.017 40.473 30.001 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 10.863 10.765 20.782 10.648 10.803 70.438 30.793 10.607 10.589 10.944 30.455 10.223 20.536 20.768 10.726 10.758 20.623 10.906 10.821 20.596 3
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
NWSYY0.678 20.779 40.782 10.774 20.637 20.827 40.491 10.736 20.597 20.561 20.947 20.438 20.206 30.610 10.758 20.667 20.773 10.594 30.880 20.824 10.673 1
DE-3DLearner LR0.608 30.853 20.689 50.593 70.483 50.830 20.466 20.652 30.528 40.482 30.954 10.288 60.250 10.448 40.595 40.532 50.748 30.503 60.822 40.806 30.647 2
Ping-Chung Yu, Cheng Sun, Min Sun: Data Efficient 3D Learner via Knowledge Transferred from 2D Model. ECCV 2022
CSC_LR_SEM0.575 40.671 80.740 30.727 30.445 60.847 10.380 70.602 50.512 50.447 50.942 40.291 50.184 40.353 80.468 80.508 60.745 40.602 20.855 30.765 50.420 8
CSG_3DSegNet0.570 50.717 60.730 40.697 40.521 30.823 50.377 80.419 80.531 30.452 40.935 80.316 30.147 50.359 70.551 70.551 40.692 70.513 50.797 60.764 60.508 4
Viewpoint_BN_LR_AIR0.566 60.780 30.659 80.677 50.484 40.799 80.419 50.636 40.480 60.432 70.940 50.238 80.124 60.396 50.609 30.432 80.735 50.527 40.787 70.752 80.423 7
PointContrast_LR_SEM0.555 70.711 70.668 60.622 60.425 70.830 20.433 40.552 60.273 80.440 60.938 60.287 70.096 70.470 30.576 50.612 30.687 80.438 80.781 80.785 40.474 5
Scratch_LR_SEM0.531 80.750 50.666 70.553 80.409 80.816 60.387 60.487 70.285 70.368 80.938 60.310 40.074 80.388 60.564 60.468 70.698 60.448 70.804 50.761 70.454 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_Ins0.649 11.000 10.800 10.721 10.603 10.807 20.044 30.735 10.377 10.466 10.550 10.605 10.550 21.000 10.506 20.776 20.618 11.000 10.526 1
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
InstTeacher3D0.598 21.000 10.727 50.205 60.420 20.833 10.405 10.470 30.247 20.463 20.536 20.559 20.533 31.000 10.552 10.782 10.587 21.000 10.444 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+CSC0.481 30.667 30.760 20.468 30.313 30.802 30.008 40.529 20.098 60.364 30.411 30.348 30.500 50.571 30.504 30.646 50.530 30.944 40.201 3
Ruihang Chu: TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation. CVPR 2022
CSC_LR_INS0.440 40.667 30.737 40.418 50.218 60.791 40.094 20.328 50.185 30.251 60.382 40.273 40.565 10.539 40.377 40.588 60.371 61.000 10.128 5
PointContrast_LR_INS0.432 50.667 30.757 30.560 20.278 50.740 50.003 60.435 40.123 50.309 40.347 50.109 60.522 40.429 60.223 60.739 30.434 50.944 40.149 4
Scratch_LR_INS0.413 60.667 30.720 60.442 40.288 40.735 60.005 50.326 60.138 40.302 50.329 60.204 50.445 60.498 50.229 50.657 40.452 40.889 60.115 6


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_ODpermissive0.355 10.833 10.754 10.611 10.067 10.699 10.036 10.149 10.425 10.177 10.248 10.033 10.355 10.095 10.143 10.750 10.245 10.600 10.167 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_DET0.126 20.667 20.293 30.082 20.007 20.328 20.000 20.000 30.030 30.016 20.006 20.000 20.001 30.000 30.000 30.363 20.073 20.400 20.000 4
PointContrast_LR_DET0.096 30.667 20.374 20.007 30.006 30.271 30.000 20.004 20.050 20.010 30.005 30.000 30.005 20.039 20.004 20.181 30.023 30.078 30.000 3
Scratch_LR_DET0.000 40.000 40.000 40.000 40.000 40.001 40.000 20.000 30.000 40.001 40.001 40.000 30.000 40.000 40.000 30.000 40.000 40.000 40.000 2