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
ActiveST0.703 10.977 10.776 20.657 40.707 10.874 10.541 10.744 10.605 10.610 10.968 10.442 30.126 50.705 10.785 10.742 10.791 10.586 10.940 10.839 10.645 1
: ActiveST.
WeakLab-3D-Net(WS3D)permissive0.662 20.812 30.762 30.742 10.635 20.828 50.474 20.736 20.588 20.546 20.947 40.450 20.174 40.536 40.752 20.668 20.735 40.583 20.902 50.797 50.573 4
DE-3DLearner LA0.639 30.839 20.723 50.681 20.629 30.839 40.424 30.728 30.538 40.526 30.945 50.427 50.120 60.511 60.643 50.547 50.781 20.566 40.905 30.809 40.607 3
Ping-Chung Yu, Cheng Sun, Min Sun: Data Efficient 3D Learner via Knowledge Transferred from 2D Model. ECCV 2022
GaIA0.638 40.536 110.783 10.651 50.600 40.840 20.413 40.728 30.490 60.520 50.948 30.475 10.299 10.518 50.680 30.629 30.729 50.573 30.906 20.815 30.626 2
LE0.608 50.791 40.726 40.651 50.589 50.779 80.346 80.662 70.493 50.524 40.923 120.430 40.234 30.572 20.638 60.411 90.708 60.533 70.855 60.782 60.508 6
One-Thing-One-Click0.594 60.756 50.722 60.494 110.546 70.795 60.371 50.725 50.559 30.488 60.957 20.367 70.261 20.547 30.575 110.225 110.671 90.543 50.904 40.826 20.557 5
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
Spec-Unc Field0.586 70.736 70.623 100.664 30.559 60.840 20.358 60.666 60.447 70.429 90.944 60.421 60.000 120.411 80.629 70.614 40.745 30.541 60.848 80.758 70.493 7
PointContrast_LA_SEM0.550 80.735 80.676 70.601 80.475 80.794 70.288 100.621 90.378 110.430 80.940 70.303 90.089 90.379 90.580 100.531 60.689 80.422 100.852 70.758 70.468 8
Viewpoint_BN_LA_AIR0.548 90.747 60.574 120.631 70.456 90.762 100.355 70.639 80.412 80.404 100.940 70.335 80.107 70.277 110.645 40.495 70.666 100.517 80.818 90.740 100.431 10
Liyi Luo, Beiwen Tian, Hao Zhao, Guyue Zhou: Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck.
CSC_LA_SEM0.531 100.659 90.638 90.578 90.417 100.775 90.254 110.537 100.396 90.439 70.939 90.284 110.083 100.414 70.599 90.488 80.698 70.444 90.785 100.747 90.440 9
SQN_LA0.486 110.587 100.649 80.527 100.372 110.718 110.320 90.510 110.393 100.325 110.924 110.290 100.095 80.287 100.607 80.356 100.626 110.416 110.672 110.680 120.359 11
Scratch_LA_SEM0.382 120.389 120.606 110.401 120.303 120.705 120.169 120.460 120.292 120.282 120.939 90.207 120.004 110.147 120.201 120.184 120.592 120.389 120.409 120.714 110.250 12


This table lists the benchmark results for the 3D semantic instance 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
WLab3D-Net_Ins(WS3D)permissive0.548 11.000 10.690 10.476 20.406 10.756 10.031 10.733 10.215 10.351 10.415 20.319 10.541 11.000 10.477 10.576 20.557 10.941 20.377 1
Box2Mask_LA0.465 20.667 20.591 30.773 10.331 20.682 20.029 20.409 20.122 20.284 20.432 10.253 20.466 21.000 10.127 40.806 10.280 30.821 40.291 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_INS0.289 30.667 20.580 40.427 30.202 30.424 50.000 30.384 30.015 30.061 40.180 40.014 30.071 40.119 50.173 30.445 30.390 20.938 30.120 3
PointContrast_LA_INS0.259 40.333 50.286 50.334 40.142 40.485 30.000 30.343 40.010 40.127 30.219 30.005 40.324 30.267 30.226 20.402 50.103 50.994 10.069 4
Scratch_LA_INS0.200 50.667 20.673 20.145 50.100 50.430 40.000 30.314 50.004 50.025 50.099 50.000 50.000 50.143 40.076 50.424 40.198 40.297 50.006 5


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
WLabel3DNet-LA(WS3D)permissive0.341 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
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


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
WeakLab3DNet (WS3D)0.682 10.863 10.765 10.782 10.648 10.803 60.438 20.793 10.607 10.589 10.944 20.455 10.223 20.536 10.768 10.726 10.758 10.623 10.906 10.821 10.596 2
DE-3DLearner LR0.608 20.853 20.689 40.593 60.483 40.830 20.466 10.652 20.528 30.482 20.954 10.288 50.250 10.448 30.595 30.532 40.748 20.503 50.822 30.806 20.647 1
Ping-Chung Yu, Cheng Sun, Min Sun: Data Efficient 3D Learner via Knowledge Transferred from 2D Model. ECCV 2022
CSC_LR_SEM0.575 30.671 70.740 20.727 20.445 50.847 10.380 60.602 40.512 40.447 40.942 30.291 40.184 30.353 70.468 70.508 50.745 30.602 20.855 20.765 40.420 7
CSG_3DSegNet0.570 40.717 50.730 30.697 30.521 20.823 40.377 70.419 70.531 20.452 30.935 70.316 20.147 40.359 60.551 60.551 30.692 60.513 40.797 50.764 50.508 3
Viewpoint_BN_LR_AIR0.566 50.780 30.659 70.677 40.484 30.799 70.419 40.636 30.480 50.432 60.940 40.238 70.124 50.396 40.609 20.432 70.735 40.527 30.787 60.752 70.423 6
PointContrast_LR_SEM0.555 60.711 60.668 50.622 50.425 60.830 20.433 30.552 50.273 70.440 50.938 50.287 60.096 60.470 20.576 40.612 20.687 70.438 70.781 70.785 30.474 4
Scratch_LR_SEM0.531 70.750 40.666 60.553 70.409 70.816 50.387 50.487 60.285 60.368 70.938 50.310 30.074 70.388 50.564 50.468 60.698 50.448 60.804 40.761 60.454 5


This table lists the benchmark results for the 3D semantic instance 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
WeakLabel3DNet(WS3D)0.649 11.000 10.800 10.721 10.603 10.807 10.044 20.735 10.377 10.466 10.550 10.605 10.550 21.000 10.506 10.776 10.618 11.000 10.526 1
TWIST+CSC0.481 20.667 20.760 20.468 30.313 20.802 20.008 30.529 20.098 50.364 20.411 20.348 20.500 40.571 20.504 20.646 40.530 20.944 30.201 2
CSC_LR_INS0.440 30.667 20.737 40.418 50.218 50.791 30.094 10.328 40.185 20.251 50.382 30.273 30.565 10.539 30.377 30.588 50.371 51.000 10.128 4
PointContrast_LR_INS0.432 40.667 20.757 30.560 20.278 40.740 40.003 50.435 30.123 40.309 30.347 40.109 50.522 30.429 50.223 50.739 20.434 40.944 30.149 3
Scratch_LR_INS0.413 50.667 20.720 50.442 40.288 30.735 50.005 40.326 50.138 30.302 40.329 50.204 40.445 50.498 40.229 40.657 30.452 30.889 50.115 5


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
WLabel3DNet-LR(WS3D)permissive0.374 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
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