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
1KP-FCNN0.7460.929600.000.9090.8220.8420.4790.9490.4000.7730.797
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas. KPConv: Flexible and Deformable Convolution for Point Clouds. In The IEEE International Conference on Computer Vision (ICCV), 2019.
2RFCR0.7780.9431.000.9420.8910.8570.5440.9500.4380.7620.837
Anonymous submission
3XPCONVP0.7780.946111.000.9710.9110.8790.5320.9480.4150.7570.811
Anonymous submission
4TBDV20.8000.9541.000.9710.9350.8680.5510.9590.5310.7530.829
Anonymous submission
5AutoNN0.7630.941838.600.9580.8870.8410.4630.9520.4550.7440.808
Anonymous submission
6PyramidPoint0.7730.9451.000.9570.9030.8440.5050.9540.4590.7270.832
https://arxiv.org/abs/2011.08692
7UDNV0.7730.945259200.000.9570.9030.8440.5050.9540.4590.7270.832
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
10RGNet0.7470.9451.000.9750.9300.8810.4810.9460.3620.7200.680
Fast Point Cloud Registration Using Semantic Segmentation - DICTA 2019
11AI_Lab-NIE0.7920.9551.000.9720.9420.8900.5720.9640.5030.7130.777
Anonymous submission
12RandLA-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)
13ds0.7740.94823.000.9560.9140.8660.5150.9570.5150.6980.768
Anonymous submission
14PAI_Conv0.7640.9441.000.9640.9040.8670.4960.9560.4540.6900.784
Anonymous submission
15SCF-Net0.7760.947563.600.9710.9180.8630.5120.9530.5050.6790.807
Anonymous submission
16BRNet_0910_20.7770.9501.000.9730.9410.8770.5220.9550.4820.6770.790
Anonymous submission
17EU_IMP0.7960.9551.000.9760.9440.9120.6280.9600.5040.6760.767
Anonymous submission
18PG_Net0.7730.94723.000.9560.9120.8810.5530.9550.4680.6610.799
Anonymous submission
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
20PAI-Conv-v20.7200.9211.000.8870.7790.8560.4660.9530.4370.6210.764
Anonymous submission
21CRFConv0.7250.9350.100.9500.9310.8640.4080.9330.3370.6050.768
Anonymous submission
22LU-10.7060.91518.450.9520.8890.8150.3620.9130.3340.6020.779
surveying and mapping institute
23CRFConv_big0.7490.9420.100.9810.9110.8180.4480.9520.4080.5940.880
Anonymous submission
2410GRU0.6730.91110800.000.9750.7690.8400.3190.9350.2620.5870.700
Anonymous submission
25new_net0.5950.879100000.000.8450.7090.7660.2610.9140.1860.5650.514
J. Contreras, J. Denzler. Edge-Convolution Point Net for Semantic Segmentation of Large-Scale Point Clouds. In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, 2019.
26DG0.6820.91223.840.9650.8690.8180.3820.9160.2870.5580.658
Anonymous submission
27MSDeepVoxNet0.6530.884115000.000.8300.6720.8380.3670.9240.3130.5000.782
Classification of Point Cloud Scenes with Multiscale Voxel Deep Network, Xavier Roynard, Jean-Emmanuel Deschaud and François Goulette
28OctFCNNet0.6480.8941200.000.9430.7560.7860.3420.9040.2570.4780.721
Anonymous submission
29shell_v20.6850.9323000.000.9730.9100.8370.3970.9310.2710.4480.715
Anonymous submission
30shellnet_v20.6930.9323000.000.9630.9040.8390.4100.9420.3470.4390.702
Z. Zhang, S. Hua, K. Yeung. ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics. In International Conference on Computer Vision (ICCV), 2019.
31GACNet0.7080.9191380.000.8640.7770.8850.6060.9420.3730.4350.778
Anonymous submission
32RF_MSSF0.6270.9031643.750.8760.8030.8180.3640.9220.2410.4260.566
H. Thomas, J. Deschaud, B. Marcotegui, F. Goulette, Y. Le Gall. Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods. In 3D Vision (3DV), 2018 International Conference on, 2018.
33NI_IMP_EU_TEST10.5560.8592400.000.8570.8280.7780.3600.8870.2280.4160.093
IMP EU SunWei
34XJTLU0.6350.894367.000.8540.7440.7460.3190.9300.2520.4150.820
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
35MS-RRFSegnet0.7250.9302560.000.8950.8360.8370.4310.9600.5360.3900.917
Anonymous submission
36SnapNet_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
37TMLC-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
38PCSNet0.7120.9431500.000.9710.9500.8790.5250.9410.3880.3550.687
39RSSP0.6470.920359.000.9160.8700.8700.5250.9300.1580.3200.589
Anonymous submission
40DLUT_SR0.5630.8601.000.9530.8490.5480.2960.8320.1920.3200.518
Anonymous submission
41OctreeNet_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.
4210GRUDeep30.4680.79010800.000.4750.4810.6920.2680.9200.1800.3090.422
Anonymous submission
43DeepVoxNet0.5710.848100000.000.8270.5310.8380.2870.8990.2360.2980.650
Classification of Point Cloud Scenes with Multiscale Voxel Deep Network, Xavier Roynard, Jean-Emmanuel Deschaud and François Goulette
44SEGCloud0.6130.8811881.000.8390.6600.8600.4050.9110.3090.2750.643
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
45ThickSeg3D0.5250.864228.010.8370.7380.5530.2600.9050.1410.2560.510
Anonymous submission
463D-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
47DeePr3SS0.5850.8890.000.8560.8320.7420.3240.8970.1850.2510.592
F. Lawin, M. Danelljan, P. Tosteberg, G. Bhat, F. Khan, M. Felsberg. Deep Projective 3D Semantic Segmentation. In , 2017.
48DeepNet0.4370.77264800.000.8380.3850.5480.0850.8410.1510.2230.423
Anonymous submission
49WYJ_JTP0.0020.0061000.000.0010.0000.0010.0050.0040.0070.0000.000
Anonymous submission
50NLNN0.2030.5391.000.0000.0000.8640.2510.5060.0000.0000.000
Anonymous submission
51TML-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

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}
 }