reduced-8 results

We use Intersection over Union (IoU) and Overall Accuracy (OA) as metrics. For more details hover the curser over the symbols or click on a classifier. In order to sort the results differently click on a symbol.

NameA_IoUOA[s]IoU 1IoU 2IoU 3IoU 4IoU 5IoU 6IoU 7IoU 8
1CRFConv_big0.7490.9420.100.9810.9110.8180.4480.9520.4080.5940.880
Anonymous submission
2DenseKPNet0.7790.9491.000.9790.9270.8880.5120.9500.4500.7090.816
Anonymous submission
3LCDE-Net0.7860.950100.000.9780.9400.8510.5180.9520.4430.7390.866
Anonymous submission
4CRFConv0.7230.9410.100.9770.9230.8480.4660.9500.4000.4050.812
Anonymous submission
5shao4770.7480.9391.000.9770.8790.8450.4370.9510.5000.6340.764
Anonymous submission
6AGMMConv0.7610.9500.100.9770.9390.8390.5000.9580.4980.5290.848
Adaptive GMM Convolution for Point Cloud Learning (BMVC2021)
7FFA-Net0.7640.949158.500.9770.9460.8540.5030.9520.4540.6120.814
Anonymous submission
8IGDC0.7830.9512.000.9770.9400.8790.5260.9530.4670.7220.799
Anonymous submission
9AnchorConv_AR0.7770.949111.000.9760.9300.8570.5310.9510.4210.7210.827
Anonymous submission
10SPC0.7520.943100.000.9760.9310.8360.4840.9470.4290.5860.826
Anonymous submission
11shao3770.7800.9511.000.9760.9250.8730.5140.9610.5260.6510.814
Anonymous submission
12AIC-Net0.7800.951383.300.9760.9250.8730.5140.9610.5260.6510.814
Anonymous submission
13EU_IMP0.7960.9551.000.9760.9440.9120.6280.9600.5040.6760.767
Anonymous submission
14LGS-Net0.7900.953432.000.9760.9460.8570.5360.9600.5190.7300.800
Anonymous submission
15SPNet0.7700.9451300.000.9750.9000.8920.5110.9510.4210.7270.786
Anonymous submission
1610GRU0.6730.91110800.000.9750.7690.8400.3190.9350.2620.5870.700
Anonymous submission
17c30.7780.946600.000.9750.9490.8700.5490.9420.4280.7200.788
Anonymous submission
18RGNet0.7470.9451.000.9750.9300.8810.4810.9460.3620.7200.680
Fast Point Cloud Registration Using Semantic Segmentation - DICTA 2019
19SPGraph0.7320.9403000.000.9740.9260.8790.4400.9320.3100.6350.762
Large-scale Point cloud segmentation with superpoint graphs, Loic Landrieu and Martin Simonovsky, CVPR2018
20BRNet_0910_20.7770.9501.000.9730.9410.8770.5220.9550.4820.6770.790
Anonymous submission
21shell_v20.6850.9323000.000.9730.9100.8370.3970.9310.2710.4480.715
Anonymous submission
22AI_Lab-NIE0.7920.9551.000.9720.9420.8900.5720.9640.5030.7130.777
Anonymous submission
23XPCONVP0.7780.946111.000.9710.9110.8790.5320.9480.4150.7570.811
Anonymous submission
24PCSNet0.7120.9431500.000.9710.9500.8790.5250.9410.3880.3550.687
25TBDV20.8000.9541.000.9710.9350.8680.5510.9590.5310.7530.829
Anonymous submission
26shao4730.7860.9521.000.9710.9340.8720.5330.9570.5260.6910.804
Anonymous submission
27SCF-Net0.7760.947563.600.9710.9180.8630.5120.9530.5050.6790.807
SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation (CVPR2021)
28DG0.6820.91223.840.9650.8690.8180.3820.9160.2870.5580.658
Anonymous submission
29PAI_Conv0.7640.9441.000.9640.9040.8670.4960.9560.4540.6900.784
Anonymous submission
30shellnet_v20.6930.9323000.000.9630.9040.8390.4100.9420.3470.4390.702
@inproceedings{zhang-shellnet-iccv19, title = {ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics}, author = {Zhiyuan Zhang and Binh-Son Hua and Sai-Kit Yeung}, booktitle = {International Conference on Computer Vision (ICCV)}, year = {2019} }
31AutoNN0.7630.941838.600.9580.8870.8410.4630.9520.4550.7440.808
Anonymous submission
32UDNV0.7730.945259200.000.9570.9030.8440.5050.9540.4590.7270.832
Anonymous submission
33PyramidPoint0.7730.9451.000.9570.9030.8440.5050.9540.4590.7270.832
https://arxiv.org/abs/2011.08692
34RandLA-Net0.7740.9481.000.9560.9140.8660.5150.9570.5150.6980.768
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds (CVPR 2020, Oral)
35ds0.7740.94823.000.9560.9140.8660.5150.9570.5150.6980.768
36DLUT_SR0.5630.8601.000.9530.8490.5480.2960.8320.1920.3200.518
Anonymous submission
37LU-10.7060.91518.450.9520.8890.8150.3620.9130.3340.6020.779
surveying and mapping institute
38PG_Net0.7720.9440.000.9470.8880.8750.5340.9530.5280.6550.793
Kangcheng Liu, and Ben M. Chen. (FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling) https://arxiv.org/abs/2012.09439
39OctFCNNet0.6480.8941200.000.9430.7560.7860.3420.9040.2570.4780.721
Anonymous submission
40RFCR0.7780.9431.000.9420.8910.8570.5440.9500.4380.7620.837
Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning (CVPR2021)
41ACNNs0.6130.9081.000.9400.8620.8040.1850.9040.2450.2900.673
Anonymous submission
42ProNet0.6160.9091.000.9400.8580.8080.2050.9080.2480.2900.673
Anonymous submission
43mpv70.7470.934265.000.9380.8520.8550.5090.9440.4000.6900.790
Anonymous submission
44RSSP0.6470.920359.000.9160.8700.8700.5250.9300.1580.3200.589
Anonymous submission
45KP-FCNN0.7460.929600.000.9090.8220.8420.4790.9490.4000.7730.797
@article{thomas2019KPConv, Author = {Thomas, Hugues and Qi, Charles R. and Deschaud, Jean-Emmanuel and Marcotegui, Beatriz and Goulette, Fran{c{c}}ois and Guibas, Leonidas J.}, Title = {KPConv: Flexible and Deformable Convolution for Point Clouds}, Journal = {The IEEE International Conference on Computer Vision (ICCV)}, Year = {2019} }
46sem3drandlanet0.6480.882136.200.9090.6420.7800.2640.9150.3010.6230.750
Anonymous submission
47OctreeNet_CRF0.5910.899184.840.9070.8200.8240.3930.9000.1090.3120.460
F. Wang, Y. Zhuang, H. Gu, and H. Hu, OctreeNet:A Novel Sparse 3D Convolutional Neural Network for Real-time 3D Outdoor Scene Analysis, submitted to IEEE Transactions on Automation Science and Engineering.
48TMLC-MSR0.5420.8621800.000.8980.7450.5370.2680.8880.1890.3640.447
Timo Hackel, Jan D. Wegner, Konrad Schindler: Fast semantic segmentation of 3d point clouds with strongly varying density. ISPRS Annals - ISPRS Congress, Prague, 2016
49FilterNet0.7310.930100.000.8980.8320.8650.5150.9510.3850.6280.778
Anonymous submission
50MS-RRFSegnet0.7250.9302560.000.8950.8360.8370.4310.9600.5360.3900.917
Anonymous submission
51PAI-Conv-v20.7200.9211.000.8870.7790.8560.4660.9530.4370.6210.764
Anonymous submission
52RF_MSSF0.6270.9031643.750.8760.8030.8180.3640.9220.2410.4260.566
@inproceedings{thomas2018semantic, title={Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods}, author={Thomas, Hugues and Deschaud, Jean-Emmanuel and Marcotegui, Beatriz and Goulette, Francois and Le Gall, Yann}, booktitle={3D Vision (3DV), 2018 International Conference on}, year={2018}, organization={IEEE} }
53GACNet0.7080.9191380.000.8640.7770.8850.6060.9420.3730.4350.778
Anonymous submission
54NI_IMP_EU_TEST10.5560.8592400.000.8570.8280.7780.3600.8870.2280.4160.093
IMP EU SunWei
55DeePr3SS0.5850.8890.000.8560.8320.7420.3240.8970.1850.2510.592
@misc{1705.03428, Author = {Felix Järemo Lawin and Martin Danelljan and Patrik Tosteberg and Goutam Bhat and Fahad Shahbaz Khan and Michael Felsberg}, Title = {Deep Projective 3D Semantic Segmentation}, Year = {2017}, Eprint = {arXiv:1705.03428}, }
56XJTLU0.6350.8945.130.8540.7440.7460.3190.9300.2520.4150.820
Cai Y, Huang H, Wang K, Zhang C, Fan L, Guo F. Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM). Remote Sensing. 2021; 13(7):1367. https://doi.org/10.3390/rs13071367
57EHNet0.7420.92111.720.8510.7550.8960.5590.9550.5080.4830.925
Semantic Segmentation of Terrestrial Laser Scanning Point Clouds Using Locally Enhanced Image-Based Geometric Representations (IEEE Transactions on Geoscience and Remote Sensing) http://dx.doi.org/10.1109/TGRS.2022.3161982
58new_net0.5950.879100000.000.8450.7090.7660.2610.9140.1860.5650.514
@inproceedings{contreras2019edge, title={Edge-Convolution Point Net for Semantic Segmentation of Large-Scale Point Clouds}, author={Contreras, Jhonatan and Denzler, Joachim}, booktitle={IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium}, pages={5236--5239}, year={2019}, organization={IEEE} }
593D-FCNN-TI0.5820.875774.000.8400.7110.7700.3180.8990.2770.2520.590
L. P. Tchapmi, C. B.Choy, I. Armeni, J. Gwak, S. Savarese, SEGCloud: Semantic Segmentation of 3D Point Clouds, International Conference on 3D Vision (3DV), 2017
60SEGCloud0.6130.8811881.000.8390.6600.8600.4050.9110.3090.2750.643
L. P. Tchapmi, C. B.Choy, I. Armeni, J. Gwak, S. Savarese, SEGCloud: Semantic Segmentation of 3D Point Clouds, International Conference on 3D Vision (3DV), 2017
61DeepNet0.4370.77264800.000.8380.3850.5480.0850.8410.1510.2230.423
Anonymous submission
62ThickSeg3D0.5250.864228.010.8370.7380.5530.2600.9050.1410.2560.510
Anonymous submission
63MSDeepVoxNet0.6530.884115000.000.8300.6720.8380.3670.9240.3130.5000.782
Classification of Point Cloud Scenes with Multiscale Voxel Deep Network, Xavier Roynard, Jean-Emmanuel Deschaud and François Goulette
64DeepVoxNet0.5710.848100000.000.8270.5310.8380.2870.8990.2360.2980.650
Classification of Point Cloud Scenes with Multiscale Voxel Deep Network, Xavier Roynard, Jean-Emmanuel Deschaud and François Goulette
65SnapNet_0.5910.8863600.000.8200.7730.7970.2290.9110.1840.3730.644
Unstructured point cloud semantic labeling using deep segmentation networks. A. Boulch, B. Le Saux and N. Audebert, Eurographics 3DOR 2017
66CASIA_VSLab0.7200.902243.060.7930.8290.9130.5360.8880.4540.6330.718
Anonymous submission
67TML-PCR0.3840.7400.000.7260.7300.4850.2240.7070.0500.0000.150
Mind the gap: modeling local and global context in (road) networks: Javier Montoya, Jan D. Wegner, Lubor Ladicky, Konrad Schindler. In: German Conference on Pattern Recognition (GCPR), Münster, Germany, 2014
6810GRUDeep30.4680.79010800.000.4750.4810.6920.2680.9200.1800.3090.422
Anonymous submission
69lixu_163_test0.0030.005231.500.0050.0000.0040.0020.0040.0050.0010.000
Anonymous submission
70WYJ_JTP0.0020.0061000.000.0010.0000.0010.0050.0040.0070.0000.000
Anonymous submission
71NLNN0.2030.5391.000.0000.0000.8640.2510.5060.0000.0000.000
Anonymous submission

References


  @inproceedings{hackel2017isprs,
   title={{SEMANTIC3D.NET: A new large-scale point cloud classification benchmark}},
   author={Timo Hackel and N. Savinov and L. Ladicky and Jan D. Wegner and K. Schindler and M. Pollefeys},
   booktitle={ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
   year = {2017},
   volume = {IV-1-W1},
   pages = {91--98}
 }