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 bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Q2E0.743 20.984 10.803 40.770 10.725 10.881 10.572 10.806 20.663 20.665 10.972 20.506 30.305 20.652 60.829 40.761 20.809 20.660 10.951 20.862 20.682 2
ActiveST0.748 10.984 10.804 30.759 50.720 20.849 50.516 20.791 30.670 10.654 20.974 10.495 50.382 10.811 10.828 50.787 10.780 60.640 20.952 10.861 30.701 1
Gengxin Liu, Oliver van Kaick, Hui Huang, Ruizhen Hu: Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation.
WS3D_LA_Sempermissive0.694 40.895 30.743 100.767 20.675 60.826 100.496 30.817 10.612 50.613 30.947 100.460 90.254 60.558 110.811 70.710 50.776 80.616 30.874 110.822 60.603 12
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
DE-3DLearner LA0.709 30.877 40.772 80.744 90.694 30.836 70.453 60.787 40.623 40.598 40.953 40.490 70.216 110.682 50.879 10.727 30.802 30.604 50.922 30.845 40.676 3
Ping-Chung Yu, Cheng Sun, Min Sun: Data Efficient 3D Learner via Knowledge Transferred from 2D Model. ECCV 2022
One-Thing-One-Click0.694 40.760 90.815 20.706 130.684 50.840 60.492 40.701 90.557 70.596 50.972 20.497 40.281 40.709 20.757 80.689 60.789 40.600 70.907 70.864 10.671 4
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
LE0.688 70.856 70.779 60.754 70.687 40.834 80.438 80.732 70.536 90.577 60.948 60.508 20.248 70.699 30.831 30.636 80.752 110.586 90.895 90.821 70.643 6
GaIA0.685 80.759 100.834 10.759 50.650 80.859 30.427 100.694 100.524 100.575 70.948 60.537 10.304 30.534 120.853 20.678 70.820 10.581 100.914 40.828 50.626 8
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
Scratch_LA_SEM0.643 120.699 130.793 50.718 120.636 100.816 110.411 110.707 80.490 120.574 80.948 60.448 100.173 130.559 100.689 120.604 110.722 120.556 110.853 120.820 80.651 5
PointContrast_LA_SEM0.653 110.717 120.775 70.754 70.626 110.804 130.391 120.689 110.485 130.572 90.945 120.448 100.232 90.603 80.813 60.591 120.775 90.537 120.885 100.816 90.608 10
Viewpoint_BN_LA_AIR0.669 90.847 80.732 110.724 110.613 120.827 90.443 70.742 60.562 60.551 100.947 100.441 120.218 100.650 70.753 90.621 90.765 100.601 60.905 80.814 120.618 9
Liyi Luo, Beiwen Tian, Hao Zhao, Guyue Zhou: Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck.
VIBUSpermissive0.691 60.860 50.731 120.738 100.672 70.860 20.470 50.766 50.625 30.547 110.949 50.491 60.255 50.693 40.715 100.712 40.778 70.597 80.911 50.816 90.635 7
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
CSC_LA_SEM0.665 100.857 60.756 90.763 40.647 90.852 40.432 90.684 120.543 80.514 120.948 60.469 80.179 120.599 90.702 110.620 100.789 40.614 40.911 50.815 110.607 11
SQN_LA0.598 130.741 110.681 130.766 30.482 130.805 120.389 130.658 130.499 110.437 130.936 130.386 130.243 80.422 130.663 130.552 130.700 130.519 130.809 130.750 130.515 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 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LA_Inspermissive0.668 11.000 10.786 10.845 10.647 10.777 10.137 10.618 20.432 10.506 10.564 20.561 10.546 31.000 10.562 10.786 20.675 11.000 10.577 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_LA0.592 21.000 10.619 50.820 20.471 20.773 20.104 20.618 10.377 20.409 20.591 10.364 20.515 40.857 20.443 30.782 30.524 41.000 10.382 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_INS0.494 30.528 40.766 30.800 30.408 40.611 30.045 30.547 40.055 50.368 30.429 30.126 30.389 50.819 30.421 40.775 40.550 21.000 10.253 4
PointContrast_LA_INS0.471 40.667 30.773 20.646 50.330 50.490 40.032 40.470 50.122 30.368 40.349 50.048 50.592 10.614 50.338 50.789 10.536 30.997 40.316 3
Scratch_LA_INS0.464 50.222 50.763 40.714 40.464 30.469 50.027 50.591 30.079 40.303 50.395 40.075 40.550 20.777 40.458 20.712 50.512 50.997 40.243 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
sort bysort bysort bysort bysort bysort bysort bysort bysort bysorted 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
sort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LR_Sem0.685 10.871 10.769 10.779 10.647 10.806 10.453 10.802 10.577 10.588 10.945 10.460 10.223 10.539 10.793 10.732 10.766 10.614 10.904 10.823 10.604 1
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
DE-3DLearner LR0.263 40.000 20.547 20.235 80.184 50.566 30.165 50.249 60.196 40.309 20.938 20.070 80.186 30.069 70.000 20.000 80.368 60.356 20.000 30.698 40.118 4
Ping-Chung Yu, Cheng Sun, Min Sun: Data Efficient 3D Learner via Knowledge Transferred from 2D Model. ECCV 2022
CSC_LR_SEM0.270 30.000 20.528 30.331 50.139 70.535 70.118 80.326 40.222 30.292 30.921 40.089 60.163 40.129 20.000 20.131 30.463 30.278 80.000 30.699 30.033 7
NWSYY0.286 20.000 20.515 40.322 60.247 20.618 20.219 30.304 50.174 80.268 40.926 30.117 20.162 50.065 80.000 20.079 50.514 20.345 40.028 20.701 20.125 2
PointContrast_LR_SEM0.253 70.000 20.412 70.347 40.137 80.564 40.140 60.361 30.187 60.249 50.914 50.092 40.055 70.102 50.000 20.048 60.392 50.302 70.000 30.697 50.056 5
Scratch_LR_SEM0.251 80.000 20.457 60.238 70.205 30.528 80.123 70.419 20.195 50.246 60.905 60.086 70.048 80.103 40.000 20.132 20.331 80.308 60.000 30.675 80.015 8
Viewpoint_BN_LR_AIR0.256 60.000 20.479 50.377 20.204 40.551 50.205 40.219 70.235 20.224 70.903 70.092 40.088 60.122 30.000 20.003 70.354 70.354 30.000 30.676 70.034 6
CSG_3DSegNet0.258 50.000 20.335 80.368 30.169 60.549 60.229 20.158 80.182 70.208 80.898 80.105 30.190 20.093 60.000 20.093 40.448 40.342 50.000 30.679 60.119 3


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 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LR_Ins0.565 10.667 10.755 10.614 10.488 10.767 10.024 20.710 10.392 10.370 10.496 10.298 10.515 10.857 10.496 10.667 10.596 10.990 10.474 1
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
InstTeacher3D0.162 30.000 20.500 40.000 50.120 30.738 20.000 30.404 30.005 50.291 20.155 30.153 20.008 60.000 20.059 20.289 30.192 40.000 20.000 4
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+CSC0.186 20.000 20.545 20.153 20.148 20.667 30.050 10.420 20.007 40.210 30.175 20.016 50.218 20.000 20.000 50.511 20.227 20.000 20.000 5
Ruihang Chu: TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation. CVPR 2022
CSC_LR_INS0.117 50.000 20.492 50.040 40.032 60.524 50.000 30.174 50.021 20.139 40.096 40.112 30.039 40.000 20.018 30.258 50.150 60.000 20.001 3
Scratch_LR_INS0.101 60.000 20.488 60.000 50.035 50.491 60.000 30.160 60.009 30.111 50.025 60.002 60.009 50.000 20.001 40.265 40.219 30.000 20.001 2
PointContrast_LR_INS0.119 40.000 20.534 30.041 30.047 40.560 40.000 30.307 40.000 60.083 60.071 50.068 40.064 30.000 20.000 50.186 60.176 50.000 20.000 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
sort bysort bysort bysort bysort bysort bysort bysort bysort bysorted 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