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	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)
3	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
4	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.
5	PCSNet	0.712	0.943	1500.00	0.971	0.950	0.879	0.525	0.941	0.388	0.355	0.687

6	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
7	NI_IMP_EU_TEST1	0.725	0.931	2400.00	0.922	0.864	0.867	0.462	0.943	0.371	0.653	0.718
IMP EU SunWei
8	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
9	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.
10	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
11	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
12	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
13	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
14	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
15	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
16	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.
17	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
18	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
19	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
20	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.
21	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.
22	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
23	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
24	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
25	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.
26	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
27	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
28	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