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
Q2E0.741 10.984 10.821 20.757 40.739 10.868 20.600 10.849 10.595 60.659 10.971 20.490 20.299 20.689 40.822 30.749 10.788 40.641 10.935 20.860 10.699 2
ActiveST0.735 20.983 20.769 40.798 10.701 20.852 50.527 20.801 20.680 10.629 20.973 10.447 100.312 10.757 10.799 40.747 20.795 30.632 20.952 10.855 20.684 3
Gengxin Liu, Oliver van Kaick, Hui Huang, Ruizhen Hu: Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation.
DE-3DLearner LA0.704 30.774 70.766 50.764 30.687 40.832 70.413 110.790 40.639 20.599 40.952 40.478 60.222 80.746 20.859 10.678 40.806 20.607 60.915 50.847 30.703 1
Ping-Chung Yu, Cheng Sun, Min Sun: Data Efficient 3D Learner via Knowledge Transferred from 2D Model. ECCV 2022
WS3D_LA_Sempermissive0.689 40.879 30.753 60.798 10.648 80.816 90.421 100.796 30.604 50.603 30.945 100.457 90.204 90.559 100.851 20.724 30.760 70.630 30.903 70.821 50.603 8
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
VIBUSpermissive0.684 50.848 40.752 70.708 90.691 30.861 30.474 50.770 50.611 40.538 90.951 50.478 60.275 40.676 50.671 110.649 80.788 40.610 50.869 90.808 100.657 4
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
GaIA0.682 60.731 110.846 10.713 80.657 60.869 10.475 40.705 90.452 130.569 50.951 50.563 10.290 30.544 110.799 40.677 50.810 10.618 40.900 80.821 50.642 5
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
LE0.680 70.744 90.731 90.727 60.664 50.859 40.427 90.759 60.562 70.562 60.948 70.480 40.245 60.735 30.765 60.648 100.786 60.591 70.931 30.817 70.624 7
One-Thing-One-Click0.670 80.734 100.815 30.661 130.644 90.841 60.509 30.741 70.479 120.548 70.968 30.461 80.251 50.664 60.754 70.656 70.744 100.541 110.917 40.844 40.625 6
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_AIR0.650 90.778 60.731 90.688 110.617 110.812 110.446 70.739 80.618 30.540 80.945 100.415 110.204 90.623 70.676 100.594 110.744 100.576 80.868 100.811 80.582 10
Liyi Luo, Beiwen Tian, Hao Zhao, Guyue Zhou: Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck.
CSC_LA_SEM0.644 100.761 80.707 120.703 100.642 100.813 100.436 80.659 110.502 90.516 110.945 100.487 30.238 70.538 120.678 90.659 60.739 120.568 100.915 50.811 80.566 12
PointContrast_LA_SEM0.636 110.694 120.738 80.731 50.653 70.817 80.467 60.651 120.517 80.522 100.946 80.479 50.198 110.575 90.526 130.649 80.747 80.569 90.845 110.803 110.600 9
Scratch_LA_SEM0.621 120.802 50.715 110.687 120.570 120.800 120.386 120.703 100.486 110.514 120.946 80.390 120.181 120.620 80.670 120.487 130.746 90.539 120.804 120.798 120.580 11
SQN_LA0.576 130.674 130.670 130.722 70.454 130.790 130.342 130.622 130.487 100.427 130.933 130.357 130.157 130.452 130.721 80.492 120.696 130.487 130.790 130.748 130.507 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 ap 25%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LA_Inspermissive0.759 11.000 10.945 20.851 10.694 10.821 20.519 20.838 10.556 20.598 20.624 10.506 10.668 11.000 10.853 10.810 40.716 11.000 10.663 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_LA0.731 21.000 10.998 10.777 40.660 30.853 10.616 10.629 30.907 10.610 10.611 20.415 20.515 41.000 10.450 50.905 10.688 20.983 40.543 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_INS0.654 31.000 10.864 40.844 20.672 20.661 30.480 30.533 50.385 30.473 40.543 30.239 30.539 30.714 50.853 10.866 20.675 31.000 10.425 5
PointContrast_LA_INS0.637 41.000 10.895 30.829 30.605 50.660 40.359 50.765 20.373 40.488 30.502 50.123 50.423 51.000 10.737 30.743 50.521 50.994 30.454 3
Scratch_LA_INS0.623 51.000 10.859 50.727 50.613 40.611 50.468 40.603 40.261 50.463 50.519 40.204 40.600 20.819 40.703 40.836 30.567 40.938 50.432 4


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 25%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LA_ODpermissive0.538 11.000 10.880 10.653 10.289 10.832 10.393 10.293 10.583 10.426 10.363 10.079 10.419 10.357 10.534 10.831 10.433 10.894 10.429 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_DET0.246 20.667 20.497 30.265 30.014 20.580 30.005 40.006 40.169 30.148 30.140 20.002 30.005 40.353 20.288 20.598 30.128 20.488 40.074 2
CSC_LA_DET0.239 30.444 30.405 40.269 20.013 40.595 20.029 20.024 20.150 40.178 20.094 30.029 20.089 30.296 40.220 30.624 20.076 40.707 20.066 3
Scratch_LA_DET0.206 40.333 40.500 20.183 40.014 30.504 40.021 30.018 30.315 20.100 40.046 40.002 40.090 20.348 30.112 40.376 40.109 30.613 30.029 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 10.863 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


3D Semantic Instance with Limited Reconstructions Benchmark

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




Method Infoavg ap 25%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LR_Ins0.773 11.000 10.885 30.783 30.738 10.840 40.402 40.793 10.804 10.605 20.676 10.636 20.593 21.000 10.805 30.894 20.761 11.000 10.696 1
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
InstTeacher3D0.738 21.000 10.770 60.714 40.583 20.885 10.608 10.636 20.649 20.654 10.612 20.637 10.541 31.000 10.824 10.896 10.639 31.000 10.634 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+CSC0.669 31.000 10.885 20.784 20.541 40.862 30.541 20.574 30.502 40.589 30.517 30.462 30.500 50.714 30.749 40.822 50.708 20.944 40.352 4
Ruihang Chu: TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation. CVPR 2022
CSC_LR_INS0.615 41.000 10.933 10.604 50.436 60.865 20.469 30.438 60.296 60.425 60.478 40.333 40.612 10.688 50.824 10.774 60.590 41.000 10.309 6
Scratch_LR_INS0.584 50.667 50.798 50.604 60.512 50.814 60.292 50.507 50.511 30.506 50.423 50.306 50.485 60.714 30.639 50.866 40.565 50.944 40.352 3
PointContrast_LR_INS0.573 60.667 50.818 40.831 10.558 30.815 50.273 60.550 40.464 50.583 40.414 60.152 60.527 40.429 60.543 60.873 30.552 60.944 40.320 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 25%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LR_ODpermissive0.551 10.833 10.866 10.763 10.341 10.847 10.433 10.283 10.629 10.445 10.449 10.158 10.459 10.343 20.439 10.816 10.487 10.842 10.482 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_DET0.252 20.667 20.670 20.101 30.084 30.634 30.002 30.033 20.370 20.097 30.059 20.001 30.030 30.498 10.122 30.459 30.097 30.577 30.040 2
CSC_LR_DET0.235 30.667 20.400 30.130 20.088 20.700 20.035 20.013 30.220 30.124 20.059 30.009 20.031 20.142 40.177 20.527 20.181 20.707 20.020 3
Scratch_LR_DET0.037 40.020 40.019 40.001 40.010 40.058 40.000 40.001 40.012 40.039 40.007 40.000 40.000 40.304 30.010 40.002 40.008 40.165 40.008 4