2D Semantic Label Results - ScanNet Benchmark

The 2D semantic labeling task involves predicting a per-pixel semantic labeling of an image.

Evaluation and metrics

Our evaluation ranks all methods according to the PASCAL VOC intersection-over-union metric (IoU). IoU = TP/(TP+FP+FN), where TP, FP, and FN are the numbers of true positive, false positive, and false negative pixels, respectively.

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

Virtual MVFusion (R)	0.745 1	0.861 1	0.839 1	0.881 1	0.672 2	0.512 1	0.422 19	0.898 1	0.723 1	0.714 1	0.954 2	0.454 1	0.509 1	0.773 1	0.895 1	0.756 1	0.820 1	0.653 1	0.935 1	0.891 1	0.728 1
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
BPNet_2D	0.670 2	0.822 3	0.795 3	0.836 2	0.659 3	0.481 2	0.451 15	0.769 5	0.656 3	0.567 4	0.931 3	0.395 6	0.390 6	0.700 4	0.534 4	0.689 11	0.770 2	0.574 3	0.865 11	0.831 3	0.675 6
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia and Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
DMMF_3d	0.605 7	0.651 10	0.744 11	0.782 3	0.637 5	0.387 4	0.536 5	0.732 10	0.590 8	0.540 6	0.856 23	0.359 12	0.306 17	0.596 16	0.539 3	0.627 22	0.706 4	0.497 8	0.785 23	0.757 21	0.476 24

MCA-Net	0.595 9	0.533 22	0.756 9	0.746 4	0.590 10	0.334 10	0.506 9	0.670 17	0.587 9	0.500 13	0.905 11	0.366 11	0.352 10	0.601 15	0.506 9	0.669 17	0.648 10	0.501 7	0.839 17	0.769 17	0.516 23

DVEFormer	0.626 5	0.616 12	0.764 6	0.690 5	0.583 11	0.322 14	0.540 3	0.809 3	0.593 7	0.502 12	0.900 14	0.374 9	0.433 3	0.660 9	0.528 5	0.665 19	0.663 6	0.491 9	0.871 10	0.810 9	0.705 4

RFBNet	0.592 10	0.616 12	0.758 8	0.659 6	0.581 12	0.330 11	0.469 13	0.655 20	0.543 15	0.524 8	0.924 4	0.355 14	0.336 12	0.572 19	0.479 11	0.671 15	0.648 10	0.480 11	0.814 21	0.814 7	0.614 12

ScanNet (2d proj)	0.330 28	0.293 27	0.521 27	0.657 7	0.361 27	0.161 27	0.250 27	0.004 28	0.440 25	0.183 28	0.836 25	0.125 27	0.060 28	0.319 28	0.132 27	0.417 27	0.412 27	0.344 27	0.541 28	0.427 28	0.109 28
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
SN_RN152pyrx8_RVC	0.546 19	0.572 19	0.663 23	0.638 8	0.518 20	0.298 18	0.366 26	0.633 23	0.510 19	0.446 21	0.864 21	0.296 22	0.267 21	0.542 21	0.346 23	0.704 8	0.575 20	0.431 21	0.853 15	0.766 19	0.630 10

UDSSEG_RVC	0.545 20	0.610 15	0.661 24	0.588 9	0.556 17	0.268 23	0.482 11	0.642 22	0.572 11	0.475 18	0.836 25	0.312 20	0.367 8	0.630 11	0.189 25	0.639 21	0.495 25	0.452 17	0.826 19	0.756 22	0.541 19

3DMV (2d proj)	0.498 24	0.481 26	0.612 25	0.579 10	0.456 24	0.343 7	0.384 23	0.623 24	0.525 18	0.381 25	0.845 24	0.254 24	0.264 23	0.557 20	0.182 26	0.581 26	0.598 17	0.429 22	0.760 25	0.661 27	0.446 26
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
EMSAFormer	0.564 18	0.581 18	0.736 12	0.564 11	0.546 18	0.219 25	0.517 7	0.675 16	0.486 21	0.427 23	0.904 12	0.352 15	0.320 15	0.589 17	0.528 5	0.708 7	0.464 26	0.413 24	0.847 16	0.786 13	0.611 13

DCRedNet	0.583 13	0.682 8	0.723 14	0.542 12	0.510 22	0.310 16	0.451 15	0.668 18	0.549 14	0.520 9	0.920 8	0.375 7	0.446 2	0.528 22	0.417 16	0.670 16	0.577 19	0.478 12	0.862 12	0.806 11	0.628 11

CMX	0.613 6	0.681 9	0.725 13	0.502 13	0.634 6	0.297 19	0.478 12	0.830 2	0.651 4	0.537 7	0.924 4	0.375 7	0.315 16	0.686 7	0.451 15	0.714 5	0.543 23	0.504 6	0.894 7	0.823 5	0.688 5

Enet (reimpl)	0.376 27	0.264 28	0.452 28	0.452 14	0.365 26	0.181 26	0.143 28	0.456 27	0.409 27	0.346 27	0.769 28	0.164 26	0.218 26	0.359 27	0.123 28	0.403 28	0.381 28	0.313 28	0.571 27	0.685 26	0.472 25
Re-implementation of Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.
SSMA	0.577 15	0.695 6	0.716 16	0.439 15	0.563 16	0.314 15	0.444 17	0.719 11	0.551 13	0.503 10	0.887 17	0.346 18	0.348 11	0.603 14	0.353 22	0.709 6	0.600 16	0.457 16	0.901 3	0.786 13	0.599 15
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
UNIV_CNP_RVC_UE	0.566 17	0.569 21	0.686 21	0.435 16	0.524 19	0.294 20	0.421 20	0.712 14	0.543 15	0.463 19	0.872 19	0.320 19	0.363 9	0.611 13	0.477 12	0.686 12	0.627 13	0.443 19	0.862 12	0.775 16	0.639 8

MVF-GNN（2D）	0.636 3	0.606 16	0.794 4	0.434 17	0.688 1	0.337 8	0.464 14	0.798 4	0.632 5	0.589 3	0.908 9	0.420 2	0.329 14	0.743 2	0.594 2	0.738 2	0.676 5	0.527 4	0.906 2	0.818 6	0.715 3

WSGFormer	0.585 12	0.706 5	0.708 18	0.434 17	0.574 14	0.283 22	0.538 4	0.759 6	0.542 17	0.482 17	0.924 4	0.351 16	0.333 13	0.614 12	0.393 18	0.692 10	0.551 22	0.461 15	0.874 9	0.809 10	0.673 7

AdapNet++	0.503 23	0.613 14	0.722 15	0.418 19	0.358 28	0.337 8	0.370 25	0.479 26	0.443 24	0.368 26	0.907 10	0.207 25	0.213 27	0.464 26	0.525 7	0.618 24	0.657 9	0.450 18	0.788 22	0.721 25	0.408 27
Abhinav Valada, Rohit Mohan, Wolfram Burgard: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation. International Journal of Computer Vision, 2019
EMSANet	0.600 8	0.716 4	0.746 10	0.395 20	0.614 9	0.382 5	0.523 6	0.713 13	0.571 12	0.503 10	0.922 7	0.404 5	0.397 5	0.655 10	0.400 17	0.626 23	0.663 6	0.469 14	0.900 4	0.827 4	0.577 16
Seichter, Daniel and Fischedick, Söhnke and Köhler, Mona and Gross, Horst-Michael: EMSANet: Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments. IJCNN 2022
ILC-PSPNet	0.475 26	0.490 25	0.581 26	0.289 21	0.507 23	0.067 28	0.379 24	0.610 25	0.417 26	0.435 22	0.822 27	0.278 23	0.267 21	0.503 24	0.228 24	0.616 25	0.533 24	0.375 25	0.820 20	0.729 23	0.560 18

DMMF	0.567 16	0.623 11	0.767 5	0.238 22	0.571 15	0.347 6	0.413 21	0.719 11	0.472 22	0.418 24	0.895 15	0.357 13	0.260 24	0.696 5	0.523 8	0.666 18	0.642 12	0.437 20	0.895 6	0.793 12	0.603 14

MIX6D_RVC	0.582 14	0.695 6	0.687 19	0.225 23	0.632 7	0.328 13	0.550 1	0.748 8	0.623 6	0.494 16	0.890 16	0.350 17	0.254 25	0.688 6	0.454 13	0.716 4	0.597 18	0.489 10	0.881 8	0.768 18	0.575 17

FuseNet	0.535 22	0.570 20	0.681 22	0.182 24	0.512 21	0.290 21	0.431 18	0.659 19	0.504 20	0.495 15	0.903 13	0.308 21	0.428 4	0.523 23	0.365 21	0.676 13	0.621 15	0.470 13	0.762 24	0.779 15	0.541 19
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 3	0.825 2	0.820 2	0.179 25	0.648 4	0.463 3	0.549 2	0.742 9	0.676 2	0.628 2	0.961 1	0.420 2	0.379 7	0.684 8	0.381 20	0.732 3	0.723 3	0.599 2	0.827 18	0.851 2	0.634 9

segfomer with 6d	0.542 21	0.594 17	0.687 19	0.146 26	0.579 13	0.308 17	0.515 8	0.703 15	0.472 22	0.498 14	0.868 20	0.369 10	0.282 19	0.589 17	0.390 19	0.701 9	0.556 21	0.416 23	0.860 14	0.759 20	0.539 21

MSeg1080_RVC	0.485 25	0.505 24	0.709 17	0.092 27	0.427 25	0.241 24	0.411 22	0.654 21	0.385 28	0.457 20	0.861 22	0.053 28	0.279 20	0.503 24	0.481 10	0.645 20	0.626 14	0.365 26	0.748 26	0.725 24	0.529 22
John Lambert, Zhuang Liu, Ozan Sener, James Hays, Vladlen Koltun: MSeg: A Composite Dataset for Multi-domain Semantic Segmentation. CVPR 2020
FAN_NV_RVC	0.586 11	0.510 23	0.764 6	0.079 28	0.620 8	0.330 11	0.494 10	0.753 7	0.573 10	0.556 5	0.884 18	0.405 4	0.303 18	0.718 3	0.452 14	0.672 14	0.658 8	0.509 5	0.898 5	0.813 8	0.727 2

2D Semantic Label Benchmark