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_Sem0.685 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
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
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
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
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
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
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


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_Ins0.347 10.593 10.448 10.322 10.224 10.610 20.004 20.488 10.144 10.187 10.311 10.121 10.342 10.424 10.198 10.473 10.333 10.773 10.249 1
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022
InstTeacher3D0.117 20.000 20.368 20.000 50.050 30.616 10.000 30.314 20.001 50.187 20.111 20.116 20.007 50.000 20.016 20.216 30.100 40.000 20.000 4
TWIST+CSC0.108 30.000 20.237 30.027 20.055 20.531 30.011 10.290 30.001 40.103 30.099 30.011 50.113 20.000 20.000 50.322 20.143 20.000 20.000 5
Ruihang Chu: TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation. CVPR 2022
PointContrast_LR_INS0.057 40.000 20.184 40.010 30.014 40.400 40.000 30.127 40.000 60.028 60.038 50.021 40.010 30.000 20.000 50.108 60.085 50.000 20.000 5
CSC_LR_INS0.056 50.000 20.177 50.006 40.009 60.370 50.000 30.070 60.003 20.059 40.054 40.052 30.010 40.000 20.004 30.122 50.072 60.000 20.000 3
Scratch_LR_INS0.054 60.000 20.153 60.000 50.011 50.350 60.000 30.108 50.001 30.042 50.010 60.001 60.001 60.000 20.000 40.153 40.141 30.000 20.000 2


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