3D Semantic Label Results - ScanNet Benchmark

The 3D semantic labeling task involves predicting a semantic labeling of a 3D scan mesh.

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. Predicted labels are evaluated per-vertex over the respective 3D scan mesh; for 3D approaches that operate on other representations like grids or points, the predicted labels should be mapped onto the mesh vertices (e.g., one such example for grid to mesh vertices is provided in the evaluation helpers).

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

SparseConvNet	0.725 1	0.647 8	0.821 1	0.846 1	0.721 1	0.869 1	0.533 1	0.754 4	0.603 4	0.614 1	0.955 1	0.572 1	0.325 1	0.710 2	0.870 2	0.724 1	0.823 1	0.628 3	0.934 1	0.865 1	0.683 1

MinkowskiNet	0.721 2	0.837 2	0.804 2	0.800 2	0.721 1	0.843 2	0.460 3	0.835 1	0.647 1	0.597 2	0.953 2	0.542 2	0.214 8	0.746 1	0.912 1	0.705 2	0.771 3	0.640 2	0.876 5	0.842 2	0.672 2
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
KP-FCNN	0.684 3	0.847 1	0.758 4	0.784 3	0.647 3	0.814 4	0.473 2	0.772 3	0.605 3	0.594 3	0.935 9	0.450 5	0.181 13	0.587 4	0.805 3	0.690 3	0.785 2	0.614 4	0.882 3	0.819 3	0.632 3

MVPNet	0.641 4	0.831 3	0.715 5	0.671 5	0.590 6	0.781 5	0.394 8	0.679 7	0.642 2	0.553 5	0.937 8	0.462 4	0.256 3	0.649 3	0.406 10	0.626 5	0.691 5	0.666 1	0.877 4	0.792 6	0.608 4

joint point-based	0.634 5	0.614 10	0.778 3	0.667 7	0.633 4	0.825 3	0.420 6	0.804 2	0.467 10	0.561 4	0.951 3	0.494 3	0.291 2	0.566 5	0.458 8	0.579 6	0.764 4	0.559 6	0.838 6	0.814 4	0.598 6

HPEIN	0.618 6	0.729 4	0.668 9	0.647 8	0.597 5	0.766 6	0.414 7	0.680 6	0.520 5	0.525 6	0.946 4	0.432 6	0.215 7	0.493 8	0.599 5	0.638 4	0.617 11	0.570 5	0.897 2	0.806 5	0.605 5

TextureNet	0.566 7	0.672 6	0.664 10	0.671 5	0.494 8	0.719 8	0.445 4	0.678 8	0.411 15	0.396 11	0.935 9	0.356 10	0.225 5	0.412 11	0.535 7	0.565 8	0.636 9	0.464 12	0.794 10	0.680 15	0.568 7

DVVNet	0.562 8	0.648 7	0.700 6	0.770 4	0.586 7	0.687 12	0.333 10	0.650 9	0.514 6	0.475 7	0.906 18	0.359 9	0.223 6	0.340 13	0.442 9	0.422 17	0.668 6	0.501 9	0.708 15	0.779 7	0.534 10

PointConv	0.556 9	0.636 9	0.640 13	0.574 15	0.472 10	0.739 7	0.430 5	0.433 12	0.418 14	0.445 9	0.944 5	0.372 8	0.185 12	0.464 9	0.575 6	0.540 9	0.639 8	0.505 8	0.827 7	0.762 8	0.515 11

PanopticFusion-label	0.529 10	0.491 18	0.688 7	0.604 12	0.386 14	0.632 18	0.225 22	0.705 5	0.434 12	0.293 16	0.815 21	0.348 11	0.241 4	0.499 7	0.669 4	0.507 10	0.649 7	0.442 16	0.796 9	0.602 20	0.561 8
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. arXiv
LAP-D	0.504 11	0.604 11	0.679 8	0.608 10	0.464 11	0.678 14	0.308 12	0.386 16	0.500 7	0.397 10	0.935 9	0.332 12	0.086 20	0.212 21	0.228 17	0.579 6	0.628 10	0.499 10	0.769 11	0.730 10	0.452 16

3DMV, FTSDF	0.501 12	0.558 15	0.608 16	0.424 21	0.478 9	0.690 11	0.246 18	0.586 10	0.468 9	0.450 8	0.911 16	0.394 7	0.160 15	0.438 10	0.212 18	0.432 16	0.541 16	0.475 11	0.742 13	0.727 11	0.477 13

PCNN	0.498 13	0.559 14	0.644 12	0.560 16	0.420 13	0.711 10	0.229 20	0.414 13	0.436 11	0.352 13	0.941 7	0.324 13	0.155 16	0.238 17	0.387 11	0.493 11	0.529 17	0.509 7	0.813 8	0.751 9	0.504 12

3DMV	0.484 14	0.484 19	0.538 19	0.643 9	0.424 12	0.606 21	0.310 11	0.574 11	0.433 13	0.378 12	0.796 22	0.301 14	0.214 8	0.537 6	0.208 19	0.472 15	0.507 20	0.413 19	0.693 16	0.602 20	0.539 9
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGB	0.458 15	0.577 13	0.611 15	0.356 23	0.321 20	0.715 9	0.299 14	0.376 17	0.328 20	0.319 14	0.944 5	0.285 16	0.164 14	0.216 20	0.229 16	0.484 13	0.545 15	0.456 14	0.755 12	0.709 12	0.475 14
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPN	0.447 16	0.679 5	0.604 17	0.578 14	0.380 15	0.682 13	0.291 15	0.106 23	0.483 8	0.258 21	0.920 14	0.258 18	0.025 23	0.231 19	0.325 12	0.480 14	0.560 14	0.463 13	0.725 14	0.666 17	0.231 23
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
SurfaceConvPF	0.442 17	0.505 17	0.622 14	0.380 22	0.342 19	0.654 16	0.227 21	0.397 15	0.367 18	0.276 18	0.924 13	0.240 19	0.198 10	0.359 12	0.262 14	0.366 19	0.581 12	0.435 17	0.640 18	0.668 16	0.398 17
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
PNET2	0.442 17	0.548 16	0.548 18	0.597 13	0.363 17	0.628 19	0.300 13	0.292 18	0.374 17	0.307 15	0.881 20	0.268 17	0.186 11	0.238 17	0.204 20	0.407 18	0.506 21	0.449 15	0.667 17	0.620 19	0.462 15

Tangent Convolutions	0.438 19	0.437 21	0.646 11	0.474 18	0.369 16	0.645 17	0.353 9	0.258 20	0.282 22	0.279 17	0.918 15	0.298 15	0.147 17	0.283 14	0.294 13	0.487 12	0.562 13	0.427 18	0.619 19	0.633 18	0.352 19
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
SPLAT Net	0.393 20	0.472 20	0.511 20	0.606 11	0.311 21	0.656 15	0.245 19	0.405 14	0.328 20	0.197 22	0.927 12	0.227 21	0.000 24	0.001 24	0.249 15	0.271 24	0.510 18	0.383 21	0.593 20	0.699 13	0.267 21
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
ScanNet+FTSDF	0.383 21	0.297 23	0.491 21	0.432 20	0.358 18	0.612 20	0.274 16	0.116 22	0.411 15	0.265 19	0.904 19	0.229 20	0.079 21	0.250 15	0.185 21	0.320 22	0.510 18	0.385 20	0.548 21	0.597 22	0.394 18

PointNet++	0.339 22	0.584 12	0.478 22	0.458 19	0.256 23	0.360 24	0.250 17	0.247 21	0.278 23	0.261 20	0.677 24	0.183 22	0.117 18	0.212 21	0.145 23	0.364 20	0.346 24	0.232 24	0.548 21	0.523 23	0.252 22
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNet	0.308 23	0.353 22	0.290 24	0.278 24	0.166 24	0.553 22	0.169 24	0.286 19	0.147 24	0.148 24	0.908 17	0.182 23	0.064 22	0.023 23	0.018 24	0.354 21	0.363 22	0.345 22	0.546 23	0.685 14	0.278 20

ScanNet	0.306 24	0.203 24	0.366 23	0.501 17	0.311 21	0.524 23	0.211 23	0.002 24	0.342 19	0.189 23	0.786 23	0.145 24	0.102 19	0.245 16	0.152 22	0.318 23	0.348 23	0.300 23	0.460 24	0.437 24	0.182 24
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17

3D Semantic label benchmark