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 bysorted 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
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
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
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
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
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
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
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
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 ap 50%bathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
sort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LA_Inspermissive0.548 10.667 20.766 10.540 20.446 10.754 10.018 30.628 10.370 10.419 10.436 20.407 10.450 30.714 20.533 20.724 10.596 10.991 20.407 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_INS0.414 30.667 20.563 50.417 30.395 20.546 30.085 10.551 20.014 40.213 40.262 40.043 40.364 40.571 30.532 30.629 20.392 20.997 10.215 3
PointContrast_LA_INS0.400 40.667 20.637 40.411 40.335 30.534 40.018 40.526 30.031 30.318 20.327 30.056 30.488 10.260 50.555 10.602 30.278 50.957 30.196 4
Box2Mask_LA0.498 21.000 10.658 20.675 10.264 40.675 20.065 20.446 40.261 20.308 30.441 10.304 20.488 21.000 10.135 50.576 40.335 30.921 50.410 1
Julian Chibane, Francis Engelmann, Tuan Anh Tran, Gerard Pons-Moll: Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes. ECCV 2022
Scratch_LA_INS0.311 50.667 20.651 30.411 50.262 50.486 50.006 50.257 50.006 50.134 50.168 50.000 50.060 50.343 40.296 40.459 50.321 40.944 40.126 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 bysorted 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
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
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
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 bysorted 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
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
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
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
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


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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
WS3D_LR_Ins0.649 11.000 10.800 10.721 10.603 10.807 20.044 30.735 10.377 10.466 10.550 10.605 10.550 21.000 10.506 20.776 20.618 11.000 10.526 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+CSC0.481 30.667 30.760 20.468 30.313 30.802 30.008 40.529 20.098 60.364 30.411 30.348 30.500 50.571 30.504 30.646 50.530 30.944 40.201 3
Ruihang Chu: TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation. CVPR 2022
InstTeacher3D0.598 21.000 10.727 50.205 60.420 20.833 10.405 10.470 30.247 20.463 20.536 20.559 20.533 31.000 10.552 10.782 10.587 21.000 10.444 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
PointContrast_LR_INS0.432 50.667 30.757 30.560 20.278 50.740 50.003 60.435 40.123 50.309 40.347 50.109 60.522 40.429 60.223 60.739 30.434 50.944 40.149 4
CSC_LR_INS0.440 40.667 30.737 40.418 50.218 60.791 40.094 20.328 50.185 30.251 60.382 40.273 40.565 10.539 40.377 40.588 60.371 61.000 10.128 5
Scratch_LR_INS0.413 60.667 30.720 60.442 40.288 40.735 60.005 50.326 60.138 40.302 50.329 60.204 50.445 60.498 50.229 50.657 40.452 40.889 60.115 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
sort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
PointContrast_LR_DET0.338 31.000 10.707 30.507 20.077 30.669 20.075 20.177 10.317 40.155 30.198 20.001 40.277 40.016 20.050 40.834 10.247 30.663 30.111 3
Scratch_LR_DET0.294 40.667 40.672 40.319 40.071 40.552 40.053 30.169 20.360 20.110 40.094 40.007 30.325 30.012 30.083 30.717 30.285 20.764 10.036 4
CSC_LR_DET0.364 21.000 10.713 20.468 30.110 10.644 30.078 10.162 30.433 10.182 20.194 30.009 20.333 20.435 10.160 10.715 40.200 40.597 40.123 2
WS3D_LR_ODpermissive0.370 11.000 10.803 10.702 10.082 20.671 10.028 40.109 40.332 30.189 10.259 10.022 10.351 10.009 40.139 20.738 20.291 10.758 20.176 1
Kangcheng Liu: WS3D: Weakly Supervised 3D Scene Segmentation with Region-Level Boundary Awareness and Instance Discrimination. European Conference on Computer Vision (ECCV), 2022