SEMANTIC-KITTI.ORG: Keyword Density Checker

Total words: 1058 | 2-word phrases: 273 | 3-word phrases: 301 | 4-word phrases: 307

PAGE INFO

Title	Try to keep the title under 60 characters (70 characters) SemanticKITTI - A Dataset for LiDAR-based Semantic Scene Understanding
Description	Try to keep the meta description between 50 - 160 characters (0 characters)
Keywords	Meta keywords are not recommended anymore (0 characters)
H1	No H1 tag on the page (0 characters)

#	Keyword	Frequency	Density
1	for semantic scene	3	1.00%
2	semantic scene understanding	3	1.00%
3	and competition link	2	0.66%
4	kitti vision benchmark	2	0.66%
5	dataset for semantic	2	0.66%
6	scene understanding using	2	0.66%
7	understanding using lidar	2	0.66%
8	using lidar sequences	2	0.66%
9	task code and	2	0.66%
10	annotation for all	2	0.66%
11	is based on	2	0.66%
12	code and competition	2	0.66%
13	a dataset for	2	0.66%
14	semantic scene completion	2	0.66%
15	panoptic segmentation link	1	0.33%
16	4d panoptic segmentation	1	0.33%
17	moving object segmentation	1	0.33%
18	added 4d panoptic	1	0.33%
19	sep 15 2020	1	0.33%
20	object segmentation link	1	0.33%
21	papers see tasks	1	0.33%
22	or the taskspecific	1	0.33%
23	taskspecific papers see	1	0.33%
24	aug 24 2020	1	0.33%
25	the taskspecific papers	1	0.33%
26	feb 01 2021	1	0.33%
27	updated semantic scene	1	0.33%
28	pdf or the	1	0.33%
29	paper pdf or	1	0.33%
30	apr 1 2020	1	0.33%

#	Keyword	Frequency	Density
1	for semantic scene understanding	3	0.98%
2	dataset for semantic scene	2	0.65%
3	semantic scene understanding using	2	0.65%
4	scene understanding using lidar	2	0.65%
5	understanding using lidar sequences	2	0.65%
6	a dataset for semantic	2	0.65%
7	code and competition link	2	0.65%
8	task code and competition	2	0.65%
9	added 4d panoptic segmentation	1	0.33%
10	codalab servers see tasks	1	0.33%
11	or the taskspecific papers	1	0.33%
12	pdf or the taskspecific	1	0.33%
13	competitions transferred to new	1	0.33%
14	transferred to new codalab	1	0.33%
15	to new codalab servers	1	0.33%
16	new codalab servers see	1	0.33%
17	if you cite our	1	0.33%
18	servers see tasks for	1	0.33%
19	see tasks for new	1	0.33%
20	paper pdf or the	1	0.33%
21	taskspecific papers see tasks	1	0.33%
22	nice if you cite	1	0.33%
23	be nice if you	1	0.33%
24	added moving object segmentation	1	0.33%
25	moving object segmentation link	1	0.33%
26	would be nice if	1	0.33%
27	the taskspecific papers see	1	0.33%
28	about dynamic objects in	1	0.33%
29	objects in the scene	1	0.33%
30	more information on the	1	0.33%
31	the dataset can be	1	0.33%
32	on the dataset can	1	0.33%
33	information on the dataset	1	0.33%
34	we annotated moving and	1	0.33%
35	annotated moving and nonmoving	1	0.33%
36	moving and nonmoving traffic	1	0.33%
37	nonmoving traffic participants with	1	0.33%
38	traffic participants with distinct	1	0.33%
39	classes including cars trucks	1	0.33%
40	dynamic objects in the	1	0.33%

#	URL	Whois	Check
1	https://arxiv.org/abs/1904.01416	Whois	arxiv.org
2	https://arxiv.org/abs/1904.01416	Whois	arxiv.org
3	https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/behley2021ijrr.pdf	Whois	uni-bonn.de