3D Semantic label benchmark
This table lists the benchmark results for the 3D semantic label scenario.
Method | Info | avg iou | bathtub | bed | bookshelf | cabinet | chair | counter | curtain | desk | door | floor | otherfurniture | picture | refrigerator | shower curtain | sink | sofa | table | toilet | wall | window |
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SparseConvNet | 0.725 1 | 0.647 5 | 0.821 1 | 0.846 1 | 0.721 1 | 0.869 1 | 0.533 1 | 0.754 4 | 0.603 2 | 0.614 2 | 0.955 1 | 0.572 1 | 0.325 1 | 0.710 1 | 0.870 2 | 0.724 1 | 0.823 1 | 0.628 2 | 0.934 1 | 0.865 1 | 0.683 1 | |
joint point-based | 0.634 4 | 0.614 7 | 0.778 2 | 0.667 6 | 0.633 4 | 0.825 2 | 0.420 5 | 0.804 1 | 0.467 6 | 0.561 3 | 0.951 2 | 0.494 4 | 0.291 2 | 0.566 3 | 0.458 7 | 0.579 4 | 0.764 2 | 0.559 4 | 0.838 4 | 0.814 4 | 0.598 4 | |
PanopticFusion-label | 0.529 8 | 0.491 14 | 0.688 6 | 0.604 9 | 0.386 11 | 0.632 14 | 0.225 18 | 0.705 5 | 0.434 8 | 0.293 13 | 0.815 17 | 0.348 9 | 0.241 3 | 0.499 6 | 0.669 4 | 0.507 7 | 0.649 6 | 0.442 12 | 0.796 7 | 0.602 16 | 0.561 6 | |
TextureNet | 0.566 5 | 0.672 3 | 0.664 7 | 0.671 5 | 0.494 6 | 0.719 6 | 0.445 2 | 0.678 6 | 0.411 11 | 0.396 8 | 0.935 8 | 0.356 8 | 0.225 4 | 0.412 9 | 0.535 6 | 0.565 5 | 0.636 8 | 0.464 9 | 0.794 8 | 0.680 12 | 0.568 5 | |
DVVNet | 0.562 6 | 0.648 4 | 0.700 5 | 0.770 3 | 0.586 5 | 0.687 10 | 0.333 8 | 0.650 7 | 0.514 4 | 0.475 5 | 0.906 14 | 0.359 7 | 0.223 5 | 0.340 11 | 0.442 8 | 0.422 13 | 0.668 5 | 0.501 7 | 0.708 11 | 0.779 5 | 0.534 8 | |
KP-FCNN | 0.694 2 | 0.849 1 | 0.770 3 | 0.810 2 | 0.685 2 | 0.813 3 | 0.438 3 | 0.791 2 | 0.566 3 | 0.616 1 | 0.944 3 | 0.500 3 | 0.216 6 | 0.559 4 | 0.880 1 | 0.690 2 | 0.758 3 | 0.627 3 | 0.922 2 | 0.832 2 | 0.613 3 | |
3DMV | 0.484 11 | 0.484 15 | 0.538 15 | 0.643 7 | 0.424 9 | 0.606 17 | 0.310 9 | 0.574 9 | 0.433 9 | 0.378 9 | 0.796 18 | 0.301 11 | 0.214 7 | 0.537 5 | 0.208 15 | 0.472 11 | 0.507 16 | 0.413 15 | 0.693 12 | 0.602 16 | 0.539 7 | |
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18 | ||||||||||||||||||||||
SurfaceConvPF | 0.442 13 | 0.505 13 | 0.622 11 | 0.380 18 | 0.342 15 | 0.654 12 | 0.227 17 | 0.397 13 | 0.367 14 | 0.276 15 | 0.924 10 | 0.240 15 | 0.198 8 | 0.359 10 | 0.262 11 | 0.366 15 | 0.581 9 | 0.435 13 | 0.640 14 | 0.668 13 | 0.398 14 | |
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames. | ||||||||||||||||||||||
PNET2 | 0.442 13 | 0.548 12 | 0.548 14 | 0.597 10 | 0.363 13 | 0.628 15 | 0.300 10 | 0.292 15 | 0.374 13 | 0.307 12 | 0.881 16 | 0.268 14 | 0.186 9 | 0.238 15 | 0.204 16 | 0.407 14 | 0.506 17 | 0.449 11 | 0.667 13 | 0.620 15 | 0.462 13 | |
PointConv | 0.556 7 | 0.636 6 | 0.640 10 | 0.574 11 | 0.472 8 | 0.739 5 | 0.430 4 | 0.433 10 | 0.418 10 | 0.445 7 | 0.944 3 | 0.372 6 | 0.185 10 | 0.464 7 | 0.575 5 | 0.540 6 | 0.639 7 | 0.505 6 | 0.827 5 | 0.762 6 | 0.515 9 | |
PointCNN with RGB | ![]() | 0.458 12 | 0.577 9 | 0.611 12 | 0.356 19 | 0.321 16 | 0.715 7 | 0.299 11 | 0.376 14 | 0.328 16 | 0.319 11 | 0.944 3 | 0.285 13 | 0.164 11 | 0.216 17 | 0.229 13 | 0.484 10 | 0.545 11 | 0.456 10 | 0.755 9 | 0.709 9 | 0.475 12 |
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018 | ||||||||||||||||||||||
3DMV, FTSDF | 0.501 9 | 0.558 11 | 0.608 13 | 0.424 17 | 0.478 7 | 0.690 9 | 0.246 14 | 0.586 8 | 0.468 5 | 0.450 6 | 0.911 12 | 0.394 5 | 0.160 12 | 0.438 8 | 0.212 14 | 0.432 12 | 0.541 12 | 0.475 8 | 0.742 10 | 0.727 8 | 0.477 11 | |
PCNN | 0.498 10 | 0.559 10 | 0.644 9 | 0.560 12 | 0.420 10 | 0.711 8 | 0.229 16 | 0.414 11 | 0.436 7 | 0.352 10 | 0.941 6 | 0.324 10 | 0.155 13 | 0.238 15 | 0.387 9 | 0.493 8 | 0.529 13 | 0.509 5 | 0.813 6 | 0.751 7 | 0.504 10 | |
Tangent Convolutions | ![]() | 0.438 15 | 0.437 17 | 0.646 8 | 0.474 14 | 0.369 12 | 0.645 13 | 0.353 7 | 0.258 17 | 0.282 18 | 0.279 14 | 0.918 11 | 0.298 12 | 0.147 14 | 0.283 12 | 0.294 10 | 0.487 9 | 0.562 10 | 0.427 14 | 0.619 15 | 0.633 14 | 0.352 16 |
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018 | ||||||||||||||||||||||
MinkowskiNet34 | 0.679 3 | 0.811 2 | 0.734 4 | 0.739 4 | 0.641 3 | 0.804 4 | 0.413 6 | 0.759 3 | 0.696 1 | 0.545 4 | 0.938 7 | 0.518 2 | 0.141 15 | 0.623 2 | 0.757 3 | 0.680 3 | 0.723 4 | 0.684 1 | 0.896 3 | 0.821 3 | 0.651 2 | |
PointNet++ | ![]() | 0.339 18 | 0.584 8 | 0.478 18 | 0.458 15 | 0.256 19 | 0.360 20 | 0.250 13 | 0.247 18 | 0.278 19 | 0.261 17 | 0.677 20 | 0.183 18 | 0.117 16 | 0.212 18 | 0.145 19 | 0.364 16 | 0.346 20 | 0.232 20 | 0.548 17 | 0.523 19 | 0.252 19 |
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space. | ||||||||||||||||||||||
ScanNet | ![]() | 0.306 20 | 0.203 20 | 0.366 19 | 0.501 13 | 0.311 17 | 0.524 19 | 0.211 19 | 0.002 20 | 0.342 15 | 0.189 19 | 0.786 19 | 0.145 20 | 0.102 17 | 0.245 14 | 0.152 18 | 0.318 19 | 0.348 19 | 0.300 19 | 0.460 20 | 0.437 20 | 0.182 20 |
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 17 | 0.297 19 | 0.491 17 | 0.432 16 | 0.358 14 | 0.612 16 | 0.274 12 | 0.116 19 | 0.411 11 | 0.265 16 | 0.904 15 | 0.229 16 | 0.079 18 | 0.250 13 | 0.185 17 | 0.320 18 | 0.510 14 | 0.385 16 | 0.548 17 | 0.597 18 | 0.394 15 | |
SSC-UNet | ![]() | 0.308 19 | 0.353 18 | 0.290 20 | 0.278 20 | 0.166 20 | 0.553 18 | 0.169 20 | 0.286 16 | 0.147 20 | 0.148 20 | 0.908 13 | 0.182 19 | 0.064 19 | 0.023 19 | 0.018 20 | 0.354 17 | 0.363 18 | 0.345 18 | 0.546 19 | 0.685 11 | 0.278 17 |
SPLAT Net | ![]() | 0.393 16 | 0.472 16 | 0.511 16 | 0.606 8 | 0.311 17 | 0.656 11 | 0.245 15 | 0.405 12 | 0.328 16 | 0.197 18 | 0.927 9 | 0.227 17 | 0.000 20 | 0.001 20 | 0.249 12 | 0.271 20 | 0.510 14 | 0.383 17 | 0.593 16 | 0.699 10 | 0.267 18 |
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 |