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




Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Q2E0.721 10.984 10.785 10.684 20.693 20.879 10.563 10.822 10.640 10.659 10.965 20.493 10.147 50.711 10.866 10.631 30.797 10.663 10.932 20.849 10.660 1
ActiveST0.703 20.977 20.776 30.657 50.707 10.874 20.541 20.744 20.605 20.610 20.968 10.442 40.126 60.705 20.785 20.742 10.791 20.586 20.940 10.839 20.645 2
Gengxin Liu, Oliver van Kaick, Hui Huang, Ruizhen Hu: Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation.
WS3D_LA_Sempermissive0.662 30.812 40.762 40.742 10.635 30.828 60.474 30.736 30.588 30.546 30.947 50.450 30.174 40.536 50.752 30.668 20.735 50.583 30.902 60.797 60.573 5
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
GaIA0.638 50.536 120.783 20.651 60.600 50.840 30.413 50.728 40.490 70.520 60.948 40.475 20.299 10.518 60.680 40.629 40.729 60.573 40.906 30.815 40.626 3
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
DE-3DLearner LA0.639 40.839 30.723 60.681 30.629 40.839 50.424 40.728 40.538 50.526 40.945 60.427 60.120 70.511 70.643 60.547 60.781 30.566 50.905 40.809 50.607 4
Ping-Chung Yu, Cheng Sun, Min Sun: Data Efficient 3D Learner via Knowledge Transferred from 2D Model. ECCV 2022
One-Thing-One-Click0.594 70.756 60.722 70.494 120.546 80.795 70.371 60.725 60.559 40.488 70.957 30.367 80.261 20.547 40.575 120.225 120.671 100.543 60.904 50.826 30.557 6
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
VIBUSpermissive0.586 80.736 80.623 110.664 40.559 70.840 30.358 70.666 70.447 80.429 100.944 70.421 70.000 130.411 90.629 80.614 50.745 40.541 70.848 90.758 80.493 8
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
LE0.608 60.791 50.726 50.651 60.589 60.779 90.346 90.662 80.493 60.524 50.923 130.430 50.234 30.572 30.638 70.411 100.708 70.533 80.855 70.782 70.508 7
Viewpoint_BN_LA_AIR0.548 100.747 70.574 130.631 80.456 100.762 110.355 80.639 90.412 90.404 110.940 80.335 90.107 80.277 120.645 50.495 80.666 110.517 90.818 100.740 110.431 11
Liyi Luo, Beiwen Tian, Hao Zhao, Guyue Zhou: Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck.
PointContrast_LA_SEM0.550 90.735 90.676 80.601 90.475 90.794 80.288 110.621 100.378 120.430 90.940 80.303 100.089 100.379 100.580 110.531 70.689 90.422 110.852 80.758 80.468 9
CSC_LA_SEM0.531 110.659 100.638 100.578 100.417 110.775 100.254 120.537 110.396 100.439 80.939 100.284 120.083 110.414 80.599 100.488 90.698 80.444 100.785 110.747 100.440 10
SQN_LA0.486 120.587 110.649 90.527 110.372 120.718 120.320 100.510 120.393 110.325 120.924 120.290 110.095 90.287 110.607 90.356 110.626 120.416 120.672 120.680 130.359 12
Scratch_LA_SEM0.382 130.389 130.606 120.401 130.303 130.705 130.169 130.460 130.292 130.282 130.939 100.207 130.004 120.147 130.201 130.184 130.592 130.389 130.409 130.714 120.250 13


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




Method Infoavg ap 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LA_Inspermissive0.548 11.000 10.690 10.476 20.406 10.756 10.031 10.733 10.215 10.351 10.415 20.319 10.541 11.000 10.477 10.576 20.557 10.941 20.377 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_LA0.465 20.667 20.591 30.773 10.331 20.682 20.029 20.409 20.122 20.284 20.432 10.253 20.466 21.000 10.127 40.806 10.280 30.821 40.291 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_INS0.289 30.667 20.580 40.427 30.202 30.424 50.000 30.384 30.015 30.061 40.180 40.014 30.071 40.119 50.173 30.445 30.390 20.938 30.120 3
PointContrast_LA_INS0.259 40.333 50.286 50.334 40.142 40.485 30.000 30.343 40.010 40.127 30.219 30.005 40.324 30.267 30.226 20.402 50.103 50.994 10.069 4
Scratch_LA_INS0.200 50.667 20.673 20.145 50.100 50.430 40.000 30.314 50.004 50.025 50.099 50.000 50.000 50.143 40.076 50.424 40.198 40.297 50.006 5


This table lists the benchmark results for the 3D object detection with limited annotations scenario.




Method Infoavg ap 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysort bysort bysort bysorted 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
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
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


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




Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sort bysort bysort bysort bysort bysort bysort bysorted 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
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
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
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
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


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




Method Infoavg ap 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysort bysort bysort bysorted 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
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
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
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
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
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
sort bysort bysort bysort bysort bysort bysort bysorted 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
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
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
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