3D Semantic Label with Limited Annotations Benchmark

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




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


3D Semantic Instance with Limited Annotations Benchmark

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




Method Infoavg apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LA_Inspermissive0.336 10.630 10.386 10.242 20.173 10.575 10.007 10.550 10.050 10.164 10.232 10.127 20.424 10.614 10.198 10.420 20.314 10.771 10.164 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_LA0.235 20.407 20.199 40.427 10.107 20.414 20.007 20.229 30.022 20.098 20.226 20.152 10.261 20.492 20.039 40.427 10.113 30.463 40.150 2
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
PointContrast_LA_INS0.124 40.037 50.109 50.085 40.052 40.287 30.000 30.219 40.001 40.040 30.104 30.002 40.074 30.211 30.055 30.239 40.036 50.661 30.020 4
Scratch_LA_INS0.089 50.306 40.319 20.025 50.035 50.274 40.000 30.157 50.001 50.006 50.031 50.000 50.000 50.095 40.017 50.172 50.077 40.094 50.002 5
CSC_LA_INS0.159 30.343 30.247 30.226 30.063 30.264 50.000 30.268 20.004 30.018 40.064 40.007 30.009 40.041 50.091 20.279 30.194 20.712 20.037 3


3D Object Detection with Limited Annotations Benchmark

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 bysorted 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
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
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
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


3D Semantic Label with Limited Reconstructions Benchmark

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




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


3D Semantic Instance with Limited Reconstructions Benchmark

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




Method Infoavg apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysort bysorted 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
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
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


3D Object Detection with Limited Reconstructions Benchmark

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 bysorted 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
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