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 apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LA_Inspermissive0.364 10.556 20.444 10.328 20.222 10.598 10.005 40.544 10.065 10.185 10.274 20.177 20.452 10.654 10.171 30.455 20.333 10.892 10.193 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_LA0.327 20.852 10.336 50.381 10.162 20.508 20.052 10.205 50.061 20.154 20.313 10.208 10.299 30.530 30.165 40.492 10.213 30.784 20.162 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_INS0.258 30.485 40.356 40.298 30.114 40.364 30.044 20.223 40.022 30.136 30.217 30.036 30.214 40.507 40.191 20.363 40.221 20.776 30.079 4
PointContrast_LA_INS0.256 40.519 30.376 30.216 50.099 50.347 40.026 30.443 20.017 40.113 50.142 40.008 50.137 50.586 20.219 10.362 50.149 50.775 40.078 5
Scratch_LA_INS0.241 50.463 50.402 20.220 40.131 30.306 50.005 40.307 30.006 50.131 40.131 50.034 40.301 20.376 50.114 50.427 30.186 40.723 50.083 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
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LA_ODpermissive0.372 10.917 10.825 10.727 10.081 10.693 10.014 30.180 20.380 10.170 30.253 10.027 10.347 10.189 30.118 10.706 20.249 10.681 20.135 2
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_DET0.306 20.667 20.720 20.426 20.059 40.560 30.053 10.138 30.301 30.191 10.130 30.015 40.220 30.326 10.053 40.727 10.149 40.642 30.127 3
PointContrast_LA_DET0.276 30.667 20.464 40.224 40.063 30.590 20.024 20.088 40.355 20.186 20.143 20.015 30.251 20.259 20.070 30.558 40.160 30.717 10.139 1
Scratch_LA_DET0.242 40.667 20.505 30.297 30.081 20.472 40.003 40.224 10.271 40.134 40.077 40.017 20.187 40.000 40.081 20.694 30.208 20.395 40.036 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 apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LR_Ins0.426 10.741 10.580 10.409 10.318 10.665 20.011 30.512 10.143 10.269 20.370 20.293 20.359 20.656 10.204 20.601 10.401 20.830 20.302 1
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
InstTeacher3D0.416 20.741 10.525 20.132 50.230 20.707 10.119 10.251 40.098 20.297 10.398 10.461 10.440 10.616 20.319 10.567 20.436 10.921 10.238 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+CSC0.295 30.537 30.396 50.148 40.140 30.625 30.003 40.439 20.023 60.159 30.251 30.166 30.228 60.444 30.193 30.435 40.324 30.689 60.117 3
Ruihang Chu: TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation. CVPR 2022
PointContrast_LR_INS0.264 40.472 60.423 30.170 20.110 40.575 60.001 60.344 30.030 50.127 40.232 50.065 60.351 30.250 50.087 50.478 30.253 40.722 30.068 4
CSC_LR_INS0.259 50.537 30.310 60.126 60.077 60.617 40.020 20.178 60.050 30.111 60.251 40.136 40.319 40.387 40.146 40.406 50.212 60.714 40.058 5
Scratch_LR_INS0.241 60.528 50.399 40.152 30.101 50.578 50.001 50.208 50.035 40.126 50.197 60.093 50.237 50.226 60.069 60.387 60.251 50.711 50.042 6


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