Benchmark Results - ScanNet Benchmark

This table lists the benchmark results for the 3D semantic label scenario.

Method	avg iou	bathtub	bed	bookshelf	cabinet	chair	counter	curtain	desk	door	floor	otherfurniture	picture	refrigerator	shower curtain	sink	sofa	table	toilet	wall	window

OccuSeg+Semantic	0.764 1	0.758 13	0.796 3	0.839 2	0.746 1	0.907 1	0.562 1	0.850 2	0.680 1	0.672 1	0.978 1	0.610 1	0.335 1	0.777 1	0.819 6	0.847 1	0.830 1	0.691 1	0.972 1	0.885 1	0.727 1

SparseConvNet	0.725 3	0.647 22	0.821 1	0.846 1	0.721 2	0.869 2	0.533 2	0.754 9	0.603 6	0.614 3	0.955 2	0.572 2	0.325 2	0.710 3	0.870 2	0.724 3	0.823 2	0.628 5	0.934 2	0.865 2	0.683 4

FusionAwareConv	0.630 16	0.604 25	0.741 13	0.766 11	0.590 14	0.747 21	0.501 6	0.734 10	0.503 18	0.527 11	0.919 28	0.454 11	0.323 3	0.550 13	0.420 24	0.678 7	0.688 15	0.544 18	0.896 5	0.795 14	0.627 10
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR2020
CU-Hybrid Net	0.693 5	0.596 26	0.789 4	0.803 6	0.677 4	0.800 10	0.469 10	0.846 3	0.554 13	0.591 6	0.948 10	0.500 4	0.316 4	0.609 6	0.847 4	0.732 2	0.808 3	0.593 8	0.894 6	0.839 4	0.652 5

DualMeshNet	0.658 9	0.778 11	0.702 16	0.806 5	0.619 10	0.813 8	0.468 11	0.693 16	0.494 20	0.524 13	0.941 15	0.449 13	0.298 5	0.510 17	0.821 5	0.675 8	0.727 10	0.568 12	0.826 18	0.803 11	0.637 8
Jonas Schult, Francis Engelmann, Theodora Kontogianni, Bastian Leibe: DualMeshNet: Joint Geodesic and Euclidean Convolutions for 3D Semantic Segmentation. CVPR 2020
joint point-based	0.634 13	0.614 24	0.778 6	0.667 21	0.633 9	0.825 5	0.420 15	0.804 4	0.467 25	0.561 7	0.951 4	0.494 5	0.291 6	0.566 12	0.458 21	0.579 21	0.764 6	0.559 15	0.838 17	0.814 7	0.598 16
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
LAP-D	0.594 20	0.720 16	0.692 21	0.637 24	0.456 26	0.773 16	0.391 22	0.730 11	0.587 8	0.445 23	0.940 17	0.381 21	0.288 7	0.434 24	0.453 22	0.591 19	0.649 20	0.581 10	0.777 25	0.749 24	0.610 12

DPC	0.592 21	0.720 16	0.700 17	0.602 27	0.480 23	0.762 19	0.380 24	0.713 14	0.585 9	0.437 24	0.940 17	0.369 23	0.288 7	0.434 24	0.509 20	0.590 20	0.639 24	0.567 13	0.772 26	0.755 22	0.592 18
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions. arXiv
MinkowskiNet	0.736 2	0.859 3	0.818 2	0.832 3	0.709 3	0.840 3	0.521 4	0.853 1	0.660 2	0.643 2	0.951 4	0.544 3	0.286 9	0.731 2	0.893 1	0.675 8	0.772 5	0.683 2	0.874 12	0.852 3	0.727 1
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
PointASNL	0.666 7	0.703 18	0.781 5	0.751 14	0.655 6	0.830 4	0.471 9	0.769 8	0.474 23	0.537 9	0.951 4	0.475 7	0.279 10	0.635 5	0.698 11	0.675 8	0.751 9	0.553 16	0.816 21	0.806 9	0.703 3
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
MVPNet	0.641 10	0.831 6	0.715 15	0.671 19	0.590 14	0.781 14	0.394 19	0.679 19	0.642 3	0.553 8	0.937 19	0.462 9	0.256 11	0.649 4	0.406 25	0.626 15	0.691 14	0.666 3	0.877 9	0.792 16	0.608 13
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
FPConv	0.639 11	0.785 9	0.760 9	0.713 15	0.603 12	0.798 12	0.392 20	0.534 28	0.603 6	0.524 13	0.948 10	0.457 10	0.250 12	0.538 14	0.723 9	0.598 18	0.696 13	0.614 6	0.872 13	0.799 12	0.567 21
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
CCRFNet	0.589 22	0.766 12	0.659 26	0.683 18	0.470 25	0.740 23	0.387 23	0.620 24	0.490 21	0.476 20	0.922 26	0.355 26	0.245 13	0.511 16	0.511 19	0.571 22	0.643 22	0.493 25	0.872 13	0.762 20	0.600 15

PanopticFusion-label	0.529 26	0.491 33	0.688 22	0.604 26	0.386 29	0.632 33	0.225 37	0.705 15	0.434 28	0.293 31	0.815 36	0.348 27	0.241 14	0.499 20	0.669 12	0.507 25	0.649 20	0.442 31	0.796 23	0.602 35	0.561 22
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
TextureNet	0.566 23	0.672 20	0.664 24	0.671 19	0.494 21	0.719 24	0.445 13	0.678 20	0.411 30	0.396 25	0.935 20	0.356 25	0.225 15	0.412 26	0.535 18	0.565 23	0.636 25	0.464 27	0.794 24	0.680 30	0.568 20
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkerhouser, Matthias Niessner, Leonidas Guibas: TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes. CVPR
DVVNet	0.562 24	0.648 21	0.700 17	0.770 10	0.586 16	0.687 28	0.333 26	0.650 21	0.514 17	0.475 21	0.906 32	0.359 24	0.223 16	0.340 29	0.442 23	0.422 32	0.668 19	0.501 23	0.708 30	0.779 18	0.534 24

HPEIN	0.618 18	0.729 15	0.668 23	0.647 22	0.597 13	0.766 18	0.414 16	0.680 18	0.520 15	0.525 12	0.946 12	0.432 16	0.215 17	0.493 22	0.599 17	0.638 13	0.617 26	0.570 11	0.897 4	0.806 9	0.605 14
Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia: Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation. ICCV 2019
3DMV	0.484 29	0.484 34	0.538 34	0.643 23	0.424 27	0.606 36	0.310 27	0.574 26	0.433 29	0.378 26	0.796 37	0.301 29	0.214 18	0.537 15	0.208 34	0.472 30	0.507 35	0.413 34	0.693 31	0.602 35	0.539 23
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
Pointnet++ & Feature	0.557 25	0.735 14	0.661 25	0.686 17	0.491 22	0.744 22	0.392 20	0.539 27	0.451 26	0.375 27	0.946 12	0.376 22	0.205 19	0.403 27	0.356 27	0.553 24	0.643 22	0.497 24	0.824 19	0.756 21	0.515 26

JSENet	0.699 4	0.881 1	0.762 8	0.821 4	0.667 5	0.800 10	0.522 3	0.792 5	0.613 4	0.607 4	0.935 20	0.492 6	0.205 19	0.576 11	0.853 3	0.691 5	0.758 7	0.652 4	0.872 13	0.828 5	0.649 6

PointConv	0.666 7	0.781 10	0.759 10	0.699 16	0.644 8	0.822 6	0.475 7	0.779 6	0.564 12	0.504 17	0.953 3	0.428 18	0.203 21	0.586 9	0.754 8	0.661 11	0.753 8	0.588 9	0.902 3	0.813 8	0.642 7
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
SurfaceConvPF	0.442 32	0.505 32	0.622 29	0.380 37	0.342 34	0.654 31	0.227 36	0.397 31	0.367 33	0.276 33	0.924 25	0.240 34	0.198 22	0.359 28	0.262 30	0.366 34	0.581 27	0.435 32	0.640 33	0.668 31	0.398 32
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
3DSM_DMMF	0.631 15	0.626 23	0.745 12	0.801 7	0.607 11	0.751 20	0.506 5	0.729 12	0.565 11	0.491 18	0.866 35	0.434 15	0.197 23	0.595 7	0.630 15	0.709 4	0.705 11	0.560 14	0.875 11	0.740 25	0.491 28

SIConv	0.625 17	0.830 7	0.694 20	0.757 12	0.563 19	0.772 17	0.448 12	0.647 22	0.520 15	0.509 15	0.949 8	0.431 17	0.191 24	0.496 21	0.614 16	0.647 12	0.672 18	0.535 21	0.876 10	0.783 17	0.571 19

PNET2	0.442 32	0.548 31	0.548 33	0.597 28	0.363 32	0.628 34	0.300 28	0.292 33	0.374 32	0.307 30	0.881 34	0.268 32	0.186 25	0.238 33	0.204 35	0.407 33	0.506 36	0.449 30	0.667 32	0.620 34	0.462 31

KP-FCNN	0.684 6	0.847 5	0.758 11	0.784 8	0.647 7	0.814 7	0.473 8	0.772 7	0.605 5	0.594 5	0.935 20	0.450 12	0.181 26	0.587 8	0.805 7	0.690 6	0.785 4	0.614 6	0.882 8	0.819 6	0.632 9
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
SConv	0.636 12	0.830 7	0.697 19	0.752 13	0.572 18	0.780 15	0.445 13	0.716 13	0.529 14	0.530 10	0.951 4	0.446 14	0.170 27	0.507 19	0.666 13	0.636 14	0.682 16	0.541 20	0.886 7	0.799 12	0.594 17

PointCNN with RGB	0.458 30	0.577 28	0.611 30	0.356 38	0.321 35	0.715 25	0.299 29	0.376 32	0.328 35	0.319 29	0.944 14	0.285 31	0.164 28	0.216 36	0.229 32	0.484 28	0.545 30	0.456 29	0.755 27	0.709 27	0.475 30
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
3DMV, FTSDF	0.501 27	0.558 30	0.608 31	0.424 36	0.478 24	0.690 27	0.246 33	0.586 25	0.468 24	0.450 22	0.911 30	0.394 20	0.160 29	0.438 23	0.212 33	0.432 31	0.541 31	0.475 26	0.742 28	0.727 26	0.477 29

PCNN	0.498 28	0.559 29	0.644 28	0.560 30	0.420 28	0.711 26	0.229 35	0.414 29	0.436 27	0.352 28	0.941 15	0.324 28	0.155 30	0.238 33	0.387 26	0.493 26	0.529 32	0.509 22	0.813 22	0.751 23	0.504 27

Tangent Convolutions	0.438 34	0.437 36	0.646 27	0.474 33	0.369 31	0.645 32	0.353 25	0.258 35	0.282 37	0.279 32	0.918 29	0.298 30	0.147 31	0.283 30	0.294 29	0.487 27	0.562 28	0.427 33	0.619 34	0.633 33	0.352 34
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
PointNet++	0.339 37	0.584 27	0.478 37	0.458 34	0.256 38	0.360 39	0.250 32	0.247 36	0.278 38	0.261 35	0.677 39	0.183 37	0.117 32	0.212 37	0.145 38	0.364 35	0.346 39	0.232 39	0.548 36	0.523 38	0.252 37
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
MCCNN	0.633 14	0.866 2	0.731 14	0.771 9	0.576 17	0.809 9	0.410 17	0.684 17	0.497 19	0.491 18	0.949 8	0.466 8	0.105 33	0.581 10	0.646 14	0.620 16	0.680 17	0.542 19	0.817 20	0.795 14	0.618 11
P. Hermosilla, T. Ritschel, P.P. Vazquez, A. Vinacua, T. Ropinski: Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds. SIGGRAPH Asia 2018
ScanNet	0.306 39	0.203 39	0.366 38	0.501 31	0.311 36	0.524 38	0.211 38	0.002 40	0.342 34	0.189 38	0.786 38	0.145 39	0.102 34	0.245 32	0.152 37	0.318 38	0.348 38	0.300 38	0.460 39	0.437 39	0.182 39
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ScanNet+FTSDF	0.383 36	0.297 38	0.491 36	0.432 35	0.358 33	0.612 35	0.274 31	0.116 37	0.411 30	0.265 34	0.904 33	0.229 35	0.079 35	0.250 31	0.185 36	0.320 37	0.510 33	0.385 35	0.548 36	0.597 37	0.394 33

SSC-UNet	0.308 38	0.353 37	0.290 39	0.278 39	0.166 39	0.553 37	0.169 39	0.286 34	0.147 39	0.148 39	0.908 31	0.182 38	0.064 36	0.023 39	0.018 40	0.354 36	0.363 37	0.345 37	0.546 38	0.685 29	0.278 35

SPH3D-GCN	0.610 19	0.858 4	0.772 7	0.489 32	0.532 20	0.792 13	0.404 18	0.643 23	0.570 10	0.507 16	0.935 20	0.414 19	0.046 37	0.510 17	0.702 10	0.602 17	0.705 11	0.549 17	0.859 16	0.773 19	0.534 24
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds.
FCPN	0.447 31	0.679 19	0.604 32	0.578 29	0.380 30	0.682 29	0.291 30	0.106 38	0.483 22	0.258 36	0.920 27	0.258 33	0.025 38	0.231 35	0.325 28	0.480 29	0.560 29	0.463 28	0.725 29	0.666 32	0.231 38
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
ERROR	0.054 40	0.000 40	0.041 40	0.172 40	0.030 40	0.062 40	0.001 40	0.035 39	0.004 40	0.051 40	0.143 40	0.019 40	0.003 39	0.041 38	0.050 39	0.003 40	0.054 40	0.018 40	0.005 40	0.264 40	0.082 40

SPLAT Net	0.393 35	0.472 35	0.511 35	0.606 25	0.311 36	0.656 30	0.245 34	0.405 30	0.328 35	0.197 37	0.927 24	0.227 36	0.000 40	0.001 40	0.249 31	0.271 39	0.510 33	0.383 36	0.593 35	0.699 28	0.267 36
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz: SPLATNet: Sparse Lattice Networks for Point Cloud Processing. CVPR 2018

This table lists the benchmark results for the 3D semantic instance scenario.

Method	avg ap 50%	bathtub	bed	bookshelf	cabinet	chair	counter	curtain	desk	door	otherfurniture	picture	refrigerator	shower curtain	sink	sofa	table	toilet	window

PointGroup	0.636 3	1.000 1	0.765 4	0.624 4	0.505 4	0.797 2	0.116 5	0.696 3	0.384 4	0.441 4	0.559 2	0.476 1	0.596 3	1.000 1	0.666 2	0.756 4	0.556 7	0.997 6	0.513 1

OccuSeg+instance	0.672 1	1.000 1	0.758 6	0.682 3	0.576 1	0.842 1	0.477 1	0.504 10	0.524 2	0.567 1	0.585 1	0.451 2	0.557 4	1.000 1	0.751 1	0.797 2	0.563 5	1.000 1	0.467 3

PanopticFusion-inst	0.478 9	0.667 11	0.712 10	0.595 5	0.259 12	0.550 12	0.000 18	0.613 5	0.175 11	0.250 13	0.434 4	0.437 3	0.411 10	0.857 6	0.485 4	0.591 12	0.267 16	0.944 8	0.359 8
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
SSEN	0.575 5	1.000 1	0.761 5	0.473 9	0.477 6	0.795 3	0.066 8	0.529 8	0.658 1	0.460 2	0.461 3	0.380 4	0.331 11	0.859 5	0.401 9	0.692 6	0.653 1	1.000 1	0.348 9

GICN	0.638 2	1.000 1	0.895 1	0.800 1	0.480 5	0.676 5	0.144 2	0.737 2	0.354 5	0.447 3	0.400 8	0.365 5	0.700 1	1.000 1	0.569 3	0.836 1	0.599 2	1.000 1	0.473 2

MPA	0.611 4	1.000 1	0.833 2	0.765 2	0.526 2	0.756 4	0.136 4	0.588 7	0.470 3	0.438 5	0.432 6	0.358 6	0.650 2	0.857 6	0.429 6	0.765 3	0.557 6	1.000 1	0.430 5

MASC	0.447 11	0.528 15	0.555 13	0.381 10	0.382 7	0.633 7	0.002 16	0.509 9	0.260 7	0.361 8	0.432 5	0.327 7	0.451 5	0.571 12	0.367 11	0.639 8	0.386 10	0.980 7	0.276 11
Chen Liu, Yasutaka Furukawa: MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.
MaskRCNN 2d->3d Proj	0.058 19	0.333 18	0.002 19	0.000 19	0.053 18	0.002 19	0.002 17	0.021 19	0.000 18	0.045 19	0.024 19	0.238 8	0.065 18	0.000 18	0.014 19	0.107 19	0.020 19	0.110 19	0.006 19

MTML	0.549 6	1.000 1	0.807 3	0.588 7	0.327 9	0.647 6	0.004 15	0.815 1	0.180 9	0.418 6	0.364 10	0.182 9	0.445 6	1.000 1	0.442 5	0.688 7	0.571 4	1.000 1	0.396 6
Jean Lahoud, Bernard Ghanem, Marc Pollefeys, Martin R. Oswald: 3D Instance Segmentation via Multi-task Metric Learning. ICCV 2019 [oral]
Occipital-SCS	0.512 7	1.000 1	0.716 8	0.509 8	0.506 3	0.611 8	0.092 7	0.602 6	0.177 10	0.346 9	0.383 9	0.165 10	0.442 7	0.850 9	0.386 10	0.618 9	0.543 8	0.889 11	0.389 7

ResNet-backbone	0.459 10	1.000 1	0.737 7	0.159 17	0.259 11	0.587 10	0.138 3	0.475 11	0.217 8	0.416 7	0.408 7	0.128 11	0.315 12	0.714 11	0.411 7	0.536 13	0.590 3	0.873 13	0.304 10

3D-BoNet	0.488 8	1.000 1	0.672 11	0.590 6	0.301 10	0.484 14	0.098 6	0.620 4	0.306 6	0.341 10	0.259 12	0.125 12	0.434 8	0.796 10	0.402 8	0.499 14	0.513 9	0.909 10	0.439 4
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. NeurIPS 2019 Spotlight
UNet-backbone	0.319 13	0.667 11	0.715 9	0.233 14	0.189 14	0.479 15	0.008 14	0.218 14	0.067 16	0.201 14	0.173 14	0.107 13	0.123 15	0.438 13	0.150 13	0.615 10	0.355 11	0.916 9	0.093 18

3D-SIS	0.382 12	1.000 1	0.432 15	0.245 13	0.190 13	0.577 11	0.013 13	0.263 13	0.033 17	0.320 11	0.240 13	0.075 14	0.422 9	0.857 6	0.117 15	0.699 5	0.271 15	0.883 12	0.235 13
Ji Hou, Angela Dai, Matthias Niessner: 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans. CVPR 2019
RegionNet	0.248 15	0.667 11	0.437 14	0.188 15	0.153 15	0.491 13	0.000 18	0.208 15	0.094 14	0.153 15	0.099 16	0.057 15	0.217 14	0.119 17	0.039 18	0.466 15	0.302 13	0.640 16	0.140 15

R-PointNet	0.306 14	0.500 16	0.405 16	0.311 11	0.348 8	0.589 9	0.054 9	0.068 17	0.126 12	0.283 12	0.290 11	0.028 16	0.219 13	0.214 16	0.331 12	0.396 16	0.275 14	0.821 15	0.245 12

3D-BEVIS	0.248 15	0.667 11	0.566 12	0.076 18	0.035 19	0.394 16	0.027 11	0.035 18	0.098 13	0.099 17	0.030 18	0.025 17	0.098 16	0.375 14	0.126 14	0.604 11	0.181 17	0.854 14	0.171 14
Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe: 3D-BEVIS: Birds-Eye-View Instance Segmentation.
Sgpn_scannet	0.143 18	0.208 19	0.390 17	0.169 16	0.065 17	0.275 18	0.029 10	0.069 16	0.000 18	0.087 18	0.043 17	0.014 18	0.027 19	0.000 18	0.112 17	0.351 17	0.168 18	0.438 18	0.138 16

Seg-Cluster	0.215 17	0.370 17	0.337 18	0.285 12	0.105 16	0.325 17	0.025 12	0.282 12	0.085 15	0.105 16	0.107 15	0.007 19	0.079 17	0.317 15	0.114 16	0.309 18	0.304 12	0.587 17	0.123 17

This table lists the benchmark results for the 2D semantic label scenario.

Method	avg iou	bathtub	bed	bookshelf	cabinet	chair	counter	curtain	desk	door	floor	otherfurniture	picture	refrigerator	shower curtain	sink	sofa	table	toilet	wall	window

DCRedNet	0.583 6	0.682 3	0.723 6	0.542 7	0.510 8	0.310 9	0.451 6	0.668 6	0.549 6	0.520 4	0.920 3	0.375 2	0.446 1	0.528 8	0.417 6	0.670 6	0.577 10	0.478 6	0.862 2	0.806 3	0.628 2

FuseNet	0.535 8	0.570 7	0.681 9	0.182 12	0.512 7	0.290 10	0.431 8	0.659 7	0.504 9	0.495 7	0.903 7	0.308 8	0.428 2	0.523 9	0.365 8	0.676 3	0.621 7	0.470 7	0.762 10	0.779 5	0.541 6
Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers: FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. ACCV 2016
CU-Hybrid-2D Net	0.636 1	0.825 1	0.820 1	0.179 13	0.648 1	0.463 1	0.549 1	0.742 1	0.676 1	0.628 1	0.961 1	0.420 1	0.379 3	0.684 1	0.381 7	0.732 1	0.723 1	0.599 1	0.827 5	0.851 1	0.634 1

MCA-Net	0.595 4	0.533 9	0.756 3	0.746 3	0.590 3	0.334 6	0.506 3	0.670 5	0.587 4	0.500 6	0.905 6	0.366 3	0.352 4	0.601 3	0.506 4	0.669 7	0.648 4	0.501 3	0.839 4	0.769 7	0.516 8

SSMA	0.577 7	0.695 2	0.716 8	0.439 9	0.563 6	0.314 8	0.444 7	0.719 3	0.551 5	0.503 5	0.887 8	0.346 7	0.348 5	0.603 2	0.353 9	0.709 2	0.600 8	0.457 8	0.901 1	0.786 4	0.599 4
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
RFBNet	0.592 5	0.616 5	0.758 2	0.659 4	0.581 5	0.330 7	0.469 5	0.655 8	0.543 7	0.524 3	0.924 2	0.355 5	0.336 6	0.572 6	0.479 5	0.671 5	0.648 4	0.480 5	0.814 7	0.814 2	0.614 3

DMMF	0.597 3	0.543 8	0.755 4	0.749 2	0.585 4	0.338 4	0.494 4	0.704 4	0.598 2	0.494 8	0.911 4	0.347 6	0.327 7	0.593 5	0.527 2	0.675 4	0.646 6	0.513 2	0.842 3	0.774 6	0.527 7

DMMF_3d	0.605 2	0.651 4	0.744 5	0.782 1	0.637 2	0.387 2	0.536 2	0.732 2	0.590 3	0.540 2	0.856 9	0.359 4	0.306 8	0.596 4	0.539 1	0.627 8	0.706 2	0.497 4	0.785 9	0.757 8	0.476 9

ILC-PSPNet	0.475 11	0.490 10	0.581 11	0.289 11	0.507 9	0.067 13	0.379 10	0.610 10	0.417 12	0.435 9	0.822 12	0.278 9	0.267 9	0.503 10	0.228 10	0.616 10	0.533 11	0.375 11	0.820 6	0.729 9	0.560 5

3DMV (2d proj)	0.498 10	0.481 11	0.612 10	0.579 6	0.456 10	0.343 3	0.384 9	0.623 9	0.525 8	0.381 10	0.845 10	0.254 10	0.264 10	0.557 7	0.182 11	0.581 11	0.598 9	0.429 10	0.760 11	0.661 12	0.446 11
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
Enet (reimpl)	0.376 12	0.264 13	0.452 13	0.452 8	0.365 11	0.181 11	0.143 13	0.456 12	0.409 13	0.346 12	0.769 13	0.164 12	0.218 11	0.359 12	0.123 13	0.403 13	0.381 13	0.313 13	0.571 12	0.685 11	0.472 10
Re-implementation of Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.
AdapNet++	0.503 9	0.613 6	0.722 7	0.418 10	0.358 13	0.337 5	0.370 11	0.479 11	0.443 10	0.368 11	0.907 5	0.207 11	0.213 12	0.464 11	0.525 3	0.618 9	0.657 3	0.450 9	0.788 8	0.721 10	0.408 12
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
ScanNet (2d proj)	0.330 13	0.293 12	0.521 12	0.657 5	0.361 12	0.161 12	0.250 12	0.004 13	0.440 11	0.183 13	0.836 11	0.125 13	0.060 13	0.319 13	0.132 12	0.417 12	0.412 12	0.344 12	0.541 13	0.427 13	0.109 13
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17

This table lists the benchmark results for the 2D semantic instance scenario.

Method	avg ap	bathtub	bed	bookshelf	cabinet	chair	counter	curtain	desk	door	otherfurniture	picture	refrigerator	shower curtain	sink	sofa	table	toilet	window

MaskRCNN_ScanNet	0.119 1	0.129 1	0.212 1	0.002 1	0.112 1	0.148 1	0.014 1	0.205 1	0.044 1	0.066 1	0.078 1	0.095 1	0.142 1	0.030 1	0.128 1	0.139 1	0.080 1	0.459 1	0.057 1
Re-implementation of Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick: Mask R-CNN. ICCV'17

This table lists the benchmark results for the scene type classification scenario.

Method	avg recall	apartment	bathroom	bedroom / hotel	bookstore / library	conference room	copy/mail room	hallway	kitchen	laundry room	living room / lounge	misc	office	storage / basement / garage

multi-task learner	0.700 1	0.500 1	1.000 1	0.882 2	0.500 1	1.000 1	1.000 1	0.500 1	1.000 1	1.000 1	0.778 1	0.000 2	0.938 1	0.000 1
Shengyu Huang, Mikhail Usvyatsov, Konrad Schindler: Indoor Scene Recognition in 3D.
SE-ResNeXt-SSMA	0.498 2	0.000 3	0.812 2	0.941 1	0.500 1	0.500 2	0.500 2	0.500 1	0.429 3	0.500 2	0.667 2	0.500 1	0.625 2	0.000 1
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. arXiv
resnet50_scannet	0.353 3	0.250 2	0.812 2	0.529 3	0.500 1	0.500 2	0.000 3	0.500 1	0.571 2	0.000 3	0.556 3	0.000 2	0.375 3	0.000 1

3D Semantic label benchmark

3D Semantic instance benchmark

2D Semantic label benchmark

2D Semantic instance benchmark

Scene type classification benchmark