Semantic3D - Results

	Name	A_IoU	OA	[s]	IoU 1	IoU 2	IoU 3	IoU 4	IoU 5	IoU 6	IoU 7	IoU 8
1	MS-RRFSegnet	0.725	0.930	2560.00	0.895	0.836	0.837	0.431	0.960	0.536	0.390	0.917
Anonymous submission
2	TBDV2	0.800	0.954	1.00	0.971	0.935	0.868	0.551	0.959	0.531	0.753	0.829
Anonymous submission
3	RandLA-Net	0.774	0.948	1.00	0.956	0.914	0.866	0.515	0.957	0.515	0.698	0.768
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds (CVPR 2020, Oral)
4	ds	0.774	0.948	23.00	0.956	0.914	0.866	0.515	0.957	0.515	0.698	0.768
Anonymous submission
5	SCF-Net	0.776	0.947	563.60	0.971	0.918	0.863	0.512	0.953	0.505	0.679	0.807
Anonymous submission
6	EU_IMP	0.796	0.955	1.00	0.976	0.944	0.912	0.628	0.960	0.504	0.676	0.767
Anonymous submission
7	AI_Lab-NIE	0.792	0.955	1.00	0.972	0.942	0.890	0.572	0.964	0.503	0.713	0.777
Anonymous submission
8	BRNet_0910_2	0.777	0.950	1.00	0.973	0.941	0.877	0.522	0.955	0.482	0.677	0.790
Anonymous submission
9	PG_Net	0.773	0.947	23.00	0.956	0.912	0.881	0.553	0.955	0.468	0.661	0.799
Anonymous submission
10	IGDC	0.783	0.951	2.00	0.977	0.940	0.879	0.526	0.953	0.467	0.722	0.799
Anonymous submission
11	UDNV	0.773	0.945	259200.00	0.957	0.903	0.844	0.505	0.954	0.459	0.727	0.832
Anonymous submission
12	PyramidPoint	0.773	0.945	1.00	0.957	0.903	0.844	0.505	0.954	0.459	0.727	0.832
https://arxiv.org/abs/2011.08692
13	AutoNN	0.763	0.941	838.60	0.958	0.887	0.841	0.463	0.952	0.455	0.744	0.808
Anonymous submission
14	PAI_Conv	0.764	0.944	1.00	0.964	0.904	0.867	0.496	0.956	0.454	0.690	0.784
Anonymous submission
15	RFCR	0.778	0.943	1.00	0.942	0.891	0.857	0.544	0.950	0.438	0.762	0.837
Anonymous submission
16	PAI-Conv-v2	0.720	0.921	1.00	0.887	0.779	0.856	0.466	0.953	0.437	0.621	0.764
Anonymous submission
17	AnchorConv_AR	0.777	0.949	111.00	0.976	0.930	0.857	0.531	0.951	0.421	0.721	0.827
Anonymous submission
18	XPCONVP	0.778	0.946	111.00	0.971	0.911	0.879	0.532	0.948	0.415	0.757	0.811
Anonymous submission
19	CRFConv_big	0.749	0.942	0.10	0.981	0.911	0.818	0.448	0.952	0.408	0.594	0.880
Anonymous submission
20	KP-FCNN	0.746	0.929	600.00	0.909	0.822	0.842	0.479	0.949	0.400	0.773	0.797
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.
21	PCSNet	0.712	0.943	1500.00	0.971	0.950	0.879	0.525	0.941	0.388	0.355	0.687

22	GACNet	0.708	0.919	1380.00	0.864	0.777	0.885	0.606	0.942	0.373	0.435	0.778
Anonymous submission
23	RGNet	0.747	0.945	1.00	0.975	0.930	0.881	0.481	0.946	0.362	0.720	0.680
Fast Point Cloud Registration Using Semantic Segmentation - DICTA 2019
24	shellnet_v2	0.693	0.932	3000.00	0.963	0.904	0.839	0.410	0.942	0.347	0.439	0.702
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.
25	CRFConv	0.725	0.935	0.10	0.950	0.931	0.864	0.408	0.933	0.337	0.605	0.768
Anonymous submission
26	LU-1	0.706	0.915	18.45	0.952	0.889	0.815	0.362	0.913	0.334	0.602	0.779
surveying and mapping institute
27	MSDeepVoxNet	0.653	0.884	115000.00	0.830	0.672	0.838	0.367	0.924	0.313	0.500	0.782
Classification of Point Cloud Scenes with Multiscale Voxel Deep Network, Xavier Roynard, Jean-Emmanuel Deschaud and François Goulette
28	SPGraph	0.732	0.940	3000.00	0.974	0.926	0.879	0.440	0.932	0.310	0.635	0.762
Large-scale Point cloud segmentation with superpoint graphs, Loic Landrieu and Martin Simonovsky, CVPR2018
29	SEGCloud	0.613	0.881	1881.00	0.839	0.660	0.860	0.405	0.911	0.309	0.275	0.643
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
30	DG	0.682	0.912	23.84	0.965	0.869	0.818	0.382	0.916	0.287	0.558	0.658
Anonymous submission
31	3D-FCNN-TI	0.582	0.875	774.00	0.840	0.711	0.770	0.318	0.899	0.277	0.252	0.590
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
32	shell_v2	0.685	0.932	3000.00	0.973	0.910	0.837	0.397	0.931	0.271	0.448	0.715
Anonymous submission
33	10GRU	0.673	0.911	10800.00	0.975	0.769	0.840	0.319	0.935	0.262	0.587	0.700
Anonymous submission
34	OctFCNNet	0.648	0.894	1200.00	0.943	0.756	0.786	0.342	0.904	0.257	0.478	0.721
Anonymous submission
35	XJTLU	0.635	0.894	367.00	0.854	0.744	0.746	0.319	0.930	0.252	0.415	0.820
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
36	RF_MSSF	0.627	0.903	1643.75	0.876	0.803	0.818	0.364	0.922	0.241	0.426	0.566
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.
37	DeepVoxNet	0.571	0.848	100000.00	0.827	0.531	0.838	0.287	0.899	0.236	0.298	0.650
Classification of Point Cloud Scenes with Multiscale Voxel Deep Network, Xavier Roynard, Jean-Emmanuel Deschaud and François Goulette
38	NI_IMP_EU_TEST1	0.556	0.859	2400.00	0.857	0.828	0.778	0.360	0.887	0.228	0.416	0.093
IMP EU SunWei
39	DLUT_SR	0.563	0.860	1.00	0.953	0.849	0.548	0.296	0.832	0.192	0.320	0.518
Anonymous submission
40	TMLC-MSR	0.542	0.862	1800.00	0.898	0.745	0.537	0.268	0.888	0.189	0.364	0.447
Timo Hackel, Jan D. Wegner, Konrad Schindler: Fast semantic segmentation of 3d point clouds with strongly varying density. ISPRS Annals - ISPRS Congress, Prague, 2016
41	new_net	0.595	0.879	100000.00	0.845	0.709	0.766	0.261	0.914	0.186	0.565	0.514
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.
42	DeePr3SS	0.585	0.889	0.00	0.856	0.832	0.742	0.324	0.897	0.185	0.251	0.592
F. Lawin, M. Danelljan, P. Tosteberg, G. Bhat, F. Khan, M. Felsberg. Deep Projective 3D Semantic Segmentation. In , 2017.
43	SnapNet_	0.591	0.886	3600.00	0.820	0.773	0.797	0.229	0.911	0.184	0.373	0.644
Unstructured point cloud semantic labeling using deep segmentation networks. A. Boulch, B. Le Saux and N. Audebert, Eurographics 3DOR 2017
44	10GRUDeep3	0.468	0.790	10800.00	0.475	0.481	0.692	0.268	0.920	0.180	0.309	0.422
Anonymous submission
45	RSSP	0.647	0.920	359.00	0.916	0.870	0.870	0.525	0.930	0.158	0.320	0.589
Anonymous submission
46	DeepNet	0.437	0.772	64800.00	0.838	0.385	0.548	0.085	0.841	0.151	0.223	0.423
Anonymous submission
47	ThickSeg3D	0.525	0.864	228.01	0.837	0.738	0.553	0.260	0.905	0.141	0.256	0.510
Anonymous submission
48	OctreeNet_CRF	0.591	0.899	184.84	0.907	0.820	0.824	0.393	0.900	0.109	0.312	0.460
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.
49	TML-PCR	0.384	0.740	0.00	0.726	0.730	0.485	0.224	0.707	0.050	0.000	0.150
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
50	WYJ_JTP	0.002	0.006	1000.00	0.001	0.000	0.001	0.005	0.004	0.007	0.000	0.000
Anonymous submission
51	NLNN	0.203	0.539	1.00	0.000	0.000	0.864	0.251	0.506	0.000	0.000	0.000
Anonymous submission

@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}
}

reduced-8 results

References