Main

processing priority

3

site type

5 (wiki-type site, growing by topic rather than chronologically)

review version

11

html import

20 (imported)

Events

first seen date

2024-08-26 06:26:42

expired found date

-

created at

2024-08-26 06:26:42

updated at

2024-11-09 00:18:18

Domain name statistics

length

16

crc

54057

tld

86

nm parts

0

nm random digits

0

nm rare letters

0

Connections

is subdomain of id

87719371 (github.io)

previous id

0

replaced with id

0

related id

-

dns primary id

0

dns alternative id

0

lifecycle status

0 (unclassified, or currently active)

Subdomains and pages

deleted subdomains

0

page imported products

0

page imported random

0

page imported parking

0

Error counters

count skipped due to recent timeouts on the same server IP

0

count content received but rejected due to 11-799

0

count dns errors

0

count cert errors

0

count timeouts

0

count http 429

0

count http 404

0

count http 403

0

count http 5xx

0

next operation date

-

Server

server bits

server ip

-

Mainpage statistics

mp import status

20

mp rejected date

-

mp saved date

-

mp size orig

13286

mp size raw text

3161

mp inner links count

0

mp inner links status

1 (no links)

Open Graph

title

description

Neural Scene Representations for Semantic Segmentation of 3D Scenes using soley 2D supervision.

image

site name

author

updated

2026-02-20 17:13:41

raw text

NeSF: Neural Semantic Fields NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes TMLR 2022 Suhani Vora *1 , Noha Radwan *1 , Klaus Greff 1 , Henning Meyer 1 , Kyle Genova 1 , Mehdi S. M. Sajjadi 1 , Etienne Pot 1 , Andrea Tagliasacchi 1,2 , Daniel Duckworth 1 1 Google Research, 2 University of Toronto * Denotes equal contribution Paper arXiv Video Code Data Abstract We present NeSF , a method for producing 3D semantic fields from posed RGB images alone. In place of classical 3D representations, our method builds on recent work in implicit neural scene representations wherein 3D structure is captured by point-wise functions. We leverage this methodology to recover 3D density fields upon which we then train a 3D semantic segmentation model supervised by posed 2D semantic maps. Despite being trained on 2D s...

Text analysis

redirect type

0 (-)

block type

0 (no issues)

detected language

1 (English)

category id

Pozostałe (16)

index version

1

spam phrases

0

Text statistics

text nonlatin

0

text cyrillic

0

text characters

2244

text words

418

text unique words

223

text lines

77

text sentences

19

text paragraphs

6

text words per sentence

22

text matched phrases

0

text matched dictionaries

0

RSS

rss path

rss status

1 (priority 1 already searched, no matches found)

rss found date

-

rss size orig

0

rss items

0

rss spam phrases

0

rss detected language

0 (awaiting analysis)

inbefore feed id

-

inbefore status

0 (new)

Sitemap

sitemap path

sitemap status

1 (priority 1 already searched, no matches found)

sitemap review version

2

sitemap urls count

0

sitemap urls adult

0

sitemap filtered products

0

sitemap filtered videos

0

sitemap found date

-

sitemap process date

-

sitemap first import date

-

sitemap last import date

-