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
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Q2E0.739 10.984 10.797 10.761 20.716 10.884 10.588 10.843 10.589 30.656 10.971 20.487 20.271 20.772 10.807 20.726 20.795 20.630 10.945 20.856 10.693 1
ActiveST0.725 20.980 20.764 40.753 30.699 20.863 20.521 20.773 20.671 10.625 20.974 10.456 60.182 90.721 20.874 10.746 10.808 10.628 20.960 10.846 20.664 3
Gengxin Liu, Oliver van Kaick, Hui Huang, Ruizhen Hu: Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation.
DE-3DLearner LA0.695 30.897 30.784 20.728 50.697 30.846 40.441 70.770 30.615 20.585 30.951 40.504 10.232 40.672 30.760 40.655 40.772 50.599 30.877 70.834 40.678 2
Ping-Chung Yu, Cheng Sun, Min Sun: Data Efficient 3D Learner via Knowledge Transferred from 2D Model. ECCV 2022
WS3D_LA_Sempermissive0.670 40.842 60.732 80.825 10.657 40.794 100.506 30.762 50.584 40.553 50.947 70.451 80.219 50.585 60.652 80.670 30.791 30.570 50.857 110.816 50.579 5
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
LE0.652 50.816 70.760 50.747 40.648 50.807 80.455 60.765 40.517 70.523 70.941 110.452 70.190 80.586 50.691 70.525 110.762 60.552 80.930 40.795 90.580 4
VIBUSpermissive0.651 60.868 40.728 110.675 90.624 70.861 30.247 130.734 60.561 50.520 80.948 60.464 40.216 60.670 40.742 50.589 90.746 70.579 40.877 70.800 70.568 6
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
GaIA0.643 70.704 110.776 30.670 100.597 90.842 50.382 90.688 90.413 120.556 40.950 50.471 30.334 10.478 100.728 60.640 50.787 40.557 70.937 30.812 60.531 11
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
One-Thing-One-Click0.642 80.725 100.735 70.717 60.635 60.829 60.457 50.639 110.421 110.552 60.967 30.460 50.240 30.558 70.788 30.621 60.720 80.477 110.915 50.842 30.539 8
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_AIR0.623 90.812 80.743 60.654 110.579 110.800 90.462 40.713 70.533 60.516 90.944 80.434 90.215 70.437 110.521 120.601 70.720 80.563 60.884 60.800 70.534 10
Liyi Luo, Beiwen Tian, Hao Zhao, Guyue Zhou: Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck.
PointContrast_LA_SEM0.614 100.844 50.731 90.681 70.590 100.791 110.348 110.689 80.503 80.502 100.942 100.361 110.154 120.484 90.624 90.591 80.708 100.524 100.874 100.793 100.536 9
CSC_LA_SEM0.612 110.747 90.731 90.679 80.603 80.815 70.400 80.648 100.453 90.481 110.944 80.421 100.173 100.504 80.623 100.588 100.690 120.545 90.877 70.778 110.541 7
SQN_LA0.542 120.568 130.674 130.618 130.462 120.772 120.351 100.567 120.443 100.378 130.931 130.335 120.173 100.392 120.623 100.455 130.688 130.466 120.769 130.720 130.450 12
Scratch_LA_SEM0.524 130.640 120.690 120.636 120.442 130.756 130.326 120.544 130.365 130.396 120.940 120.284 130.085 130.333 130.479 130.502 120.696 110.453 130.785 120.746 120.372 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
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LA_Inspermissive0.341 10.593 20.420 10.364 20.175 10.578 10.004 30.456 10.092 10.194 10.267 20.164 10.330 10.390 20.186 10.523 10.315 10.858 10.221 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_LA0.289 20.741 10.274 40.418 10.093 50.427 20.015 20.290 30.046 20.116 30.272 10.158 20.273 20.613 10.066 50.364 30.136 50.707 30.192 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_INS0.229 30.556 40.300 30.240 30.133 20.347 30.026 10.286 40.003 40.071 40.132 40.012 40.122 40.305 30.181 20.301 40.194 20.826 20.087 3
PointContrast_LA_INS0.216 40.593 20.259 50.110 50.129 30.338 40.003 50.347 20.008 30.120 20.149 30.014 30.227 30.118 50.175 30.372 20.149 40.691 40.086 4
Scratch_LA_INS0.147 50.111 50.309 20.119 40.093 40.315 50.003 40.175 50.001 50.044 50.069 50.000 50.019 50.187 40.094 40.286 50.162 30.624 50.038 5


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
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


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
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


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
sorted bysort bysort bysort bysort bysort 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
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
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


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
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