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
1PCSNet0.7120.9431500.000.9710.9500.8790.5250.9410.3880.3550.687
2EU_IMP0.7960.9551.000.9760.9440.9120.6280.9600.5040.6760.767
Anonymous submission
3AI_Lab-NIE0.7920.9551.000.9720.9420.8900.5720.9640.5030.7130.777
Anonymous submission
4BRNet_0910_20.7770.9501.000.9730.9410.8770.5220.9550.4820.6770.790
Anonymous submission
5IGDC0.7830.9512.000.9770.9400.8790.5260.9530.4670.7220.799
Anonymous submission
6AGMMConv0.7610.9500.100.9770.9390.8390.5000.9580.4980.5290.848
Anonymous submission
7TBDV20.8000.9541.000.9710.9350.8680.5510.9590.5310.7530.829
Anonymous submission
8RGNet0.7470.9451.000.9750.9300.8810.4810.9460.3620.7200.680
Fast Point Cloud Registration Using Semantic Segmentation - DICTA 2019
9AnchorConv_AR0.7770.949111.000.9760.9300.8570.5310.9510.4210.7210.827
Anonymous submission
10SPGraph0.7320.9403000.000.9740.9260.8790.4400.9320.3100.6350.762
Large-scale Point cloud segmentation with superpoint graphs, Loic Landrieu and Martin Simonovsky, CVPR2018
11CRFConv0.7230.9410.100.9770.9230.8480.4660.9500.4000.4050.812
Anonymous submission
12SCF-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)
13ds0.7740.94823.000.9560.9140.8660.5150.9570.5150.6980.768
Anonymous submission
14RandLA-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)
15XPCONVP0.7780.946111.000.9710.9110.8790.5320.9480.4150.7570.811
Anonymous submission
16CRFConv_big0.7490.9420.100.9810.9110.8180.4480.9520.4080.5940.880
Anonymous submission
17shell_v20.6850.9323000.000.9730.9100.8370.3970.9310.2710.4480.715
Anonymous submission
18PAI_Conv0.7640.9441.000.9640.9040.8670.4960.9560.4540.6900.784
Anonymous submission
19shellnet_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.
20UDNV0.7730.945259200.000.9570.9030.8440.5050.9540.4590.7270.832
Anonymous submission
21PyramidPoint0.7730.9451.000.9570.9030.8440.5050.9540.4590.7270.832
https://arxiv.org/abs/2011.08692
22SPNet0.7700.9451300.000.9750.9000.8920.5110.9510.4210.7270.786
Anonymous submission
23RFCR0.7780.9431.000.9420.8910.8570.5440.9500.4380.7620.837
Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning (CVPR2021)
24LU-10.7060.91518.450.9520.8890.8150.3620.9130.3340.6020.779
surveying and mapping institute
25PG_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
26AutoNN0.7630.941838.600.9580.8870.8410.4630.9520.4550.7440.808
Anonymous submission
27RSSP0.6470.920359.000.9160.8700.8700.5250.9300.1580.3200.589
Anonymous submission
28DG0.6820.91223.840.9650.8690.8180.3820.9160.2870.5580.658
Anonymous submission
29ACNNs0.6130.9081.000.9400.8620.8040.1850.9040.2450.2900.673
Anonymous submission
30DLUT_SR0.5630.8601.000.9530.8490.5480.2960.8320.1920.3200.518
Anonymous submission
31MS-RRFSegnet0.7250.9302560.000.8950.8360.8370.4310.9600.5360.3900.917
Anonymous submission
32DeePr3SS0.5850.8890.000.8560.8320.7420.3240.8970.1850.2510.592
F. Lawin, M. Danelljan, P. Tosteberg, G. Bhat, F. Khan, M. Felsberg. Deep Projective 3D Semantic Segmentation. In , 2017.
33FilterNet0.7310.930100.000.8980.8320.8650.5150.9510.3850.6280.778
Anonymous submission
34CASIA_VSLab0.7200.902243.060.7930.8290.9130.5360.8880.4540.6330.718
Anonymous submission
35NI_IMP_EU_TEST10.5560.8592400.000.8570.8280.7780.3600.8870.2280.4160.093
IMP EU SunWei
36KP-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.
37OctreeNet_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.
38RF_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.
39PAI-Conv-v20.7200.9211.000.8870.7790.8560.4660.9530.4370.6210.764
Anonymous submission
40GACNet0.7080.9191380.000.8640.7770.8850.6060.9420.3730.4350.778
Anonymous submission
41SnapNet_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
4210GRU0.6730.91110800.000.9750.7690.8400.3190.9350.2620.5870.700
Anonymous submission
43OctFCNNet0.6480.8941200.000.9430.7560.7860.3420.9040.2570.4780.721
Anonymous submission
44TMLC-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
45XJTLU0.6350.894367.000.8540.7440.7460.3190.9300.2520.4150.820
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
46ThickSeg3D0.5250.864228.010.8370.7380.5530.2600.9050.1410.2560.510
Anonymous submission
47TML-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
483D-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
49new_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.
50MSDeepVoxNet0.6530.884115000.000.8300.6720.8380.3670.9240.3130.5000.782
Classification of Point Cloud Scenes with Multiscale Voxel Deep Network, Xavier Roynard, Jean-Emmanuel Deschaud and François Goulette
51SEGCloud0.6130.8811881.000.8390.6600.8600.4050.9110.3090.2750.643
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
52DeepVoxNet0.5710.848100000.000.8270.5310.8380.2870.8990.2360.2980.650
Classification of Point Cloud Scenes with Multiscale Voxel Deep Network, Xavier Roynard, Jean-Emmanuel Deschaud and François Goulette
5310GRUDeep30.4680.79010800.000.4750.4810.6920.2680.9200.1800.3090.422
Anonymous submission
54DeepNet0.4370.77264800.000.8380.3850.5480.0850.8410.1510.2230.423
Anonymous submission
55WYJ_JTP0.0020.0061000.000.0010.0000.0010.0050.0040.0070.0000.000
Anonymous submission
56NLNN0.2030.5391.000.0000.0000.8640.2510.5060.0000.0000.000
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}
 }