Semantic3D - Results

	Name	A_IoU	OA	[s]	IoU 1	IoU 2	IoU 3	IoU 4	IoU 5	IoU 6	IoU 7	IoU 8
1	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
2	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
3	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
4	LGS-Net	0.790	0.953	432.00	0.976	0.946	0.857	0.536	0.960	0.519	0.730	0.800
Anonymous submission
5	shao473	0.786	0.952	1.00	0.971	0.934	0.872	0.533	0.957	0.526	0.691	0.804
Anonymous submission
6	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
7	shao377	0.780	0.951	1.00	0.976	0.925	0.873	0.514	0.961	0.526	0.651	0.814
Anonymous submission
8	AIC-Net	0.780	0.951	383.30	0.976	0.925	0.873	0.514	0.961	0.526	0.651	0.814
Anonymous submission
9	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
10	AGMMConv	0.761	0.950	0.10	0.977	0.939	0.839	0.500	0.958	0.498	0.529	0.848
Adaptive GMM Convolution for Point Cloud Learning (BMVC2021)
11	LCDE-Net	0.786	0.950	100.00	0.978	0.940	0.851	0.518	0.952	0.443	0.739	0.866
Anonymous submission
12	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
13	FFA-Net	0.764	0.949	158.50	0.977	0.946	0.854	0.503	0.952	0.454	0.612	0.814
Anonymous submission
14	DenseKPNet	0.779	0.949	1.00	0.979	0.927	0.888	0.512	0.950	0.450	0.709	0.816
Anonymous submission
15	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)
16	ds	0.774	0.948	23.00	0.956	0.914	0.866	0.515	0.957	0.515	0.698	0.768

17	SCF-Net	0.776	0.947	563.60	0.971	0.918	0.863	0.512	0.953	0.505	0.679	0.807
SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation (CVPR2021)
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	c3	0.778	0.946	600.00	0.975	0.949	0.870	0.549	0.942	0.428	0.720	0.788
Anonymous submission
20	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
21	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
22	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
23	SPNet	0.770	0.945	1300.00	0.975	0.900	0.892	0.511	0.951	0.421	0.727	0.786
Anonymous submission
24	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
25	PG_Net	0.772	0.944	0.00	0.947	0.888	0.875	0.534	0.953	0.528	0.655	0.793
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
26	PCSNet	0.712	0.943	1500.00	0.971	0.950	0.879	0.525	0.941	0.388	0.355	0.687

27	RFCR	0.778	0.943	1.00	0.942	0.891	0.857	0.544	0.950	0.438	0.762	0.837
Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning (CVPR2021)
28	SPC	0.752	0.943	100.00	0.976	0.931	0.836	0.484	0.947	0.429	0.586	0.826
Anonymous submission
29	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
30	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
31	CRFConv	0.723	0.941	0.10	0.977	0.923	0.848	0.466	0.950	0.400	0.405	0.812
Anonymous submission
32	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
33	shao477	0.748	0.939	1.00	0.977	0.879	0.845	0.437	0.951	0.500	0.634	0.764
Anonymous submission
34	mpv7	0.747	0.934	265.00	0.938	0.852	0.855	0.509	0.944	0.400	0.690	0.790
Anonymous submission
35	shellnet_v2	0.693	0.932	3000.00	0.963	0.904	0.839	0.410	0.942	0.347	0.439	0.702
@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} }
36	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
37	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
38	FilterNet	0.731	0.930	100.00	0.898	0.832	0.865	0.515	0.951	0.385	0.628	0.778
Anonymous submission
39	KP-FCNN	0.746	0.929	600.00	0.909	0.822	0.842	0.479	0.949	0.400	0.773	0.797
@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} }
40	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
41	EHNet	0.742	0.921	11.72	0.851	0.755	0.896	0.559	0.955	0.508	0.483	0.925
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
42	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
43	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
44	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
45	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
46	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
47	ProNet	0.616	0.909	1.00	0.940	0.858	0.808	0.205	0.908	0.248	0.290	0.673
Anonymous submission
48	ACNNs	0.613	0.908	1.00	0.940	0.862	0.804	0.185	0.904	0.245	0.290	0.673
Anonymous submission
49	RF_MSSF	0.627	0.903	1643.75	0.876	0.803	0.818	0.364	0.922	0.241	0.426	0.566
@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} }
50	CASIA_VSLab	0.720	0.902	243.06	0.793	0.829	0.913	0.536	0.888	0.454	0.633	0.718
Anonymous submission
51	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, OctreeNetï¼šA Novel Sparse 3D Convolutional Neural Network for Real-time 3D Outdoor Scene Analysis, submitted to IEEE Transactions on Automation Science and Engineering.
52	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
53	XJTLU	0.635	0.894	5.13	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
54	DeePr3SS	0.585	0.889	0.00	0.856	0.832	0.742	0.324	0.897	0.185	0.251	0.592
@misc{1705.03428, Author = {Felix JÃ¤remo 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}, }
55	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
56	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 FranÃ§ois Goulette
57	sem3drandlanet	0.648	0.882	136.20	0.909	0.642	0.780	0.264	0.915	0.301	0.623	0.750
Anonymous submission
58	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
59	new_net	0.595	0.879	100000.00	0.845	0.709	0.766	0.261	0.914	0.186	0.565	0.514
@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} }
60	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
61	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
62	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
63	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
64	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
65	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 FranÃ§ois Goulette
66	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
67	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
68	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), MÃ¼nster, Germany, 2014
69	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
70	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
71	lixu_163_test	0.003	0.005	231.50	0.005	0.000	0.004	0.002	0.004	0.005	0.001	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