id
related bits
0
processing priority
3
site type
5 (wiki-type site, growing by topic rather than chronologically)
review version
11
html import
20 (imported)
first seen date
2024-10-14 19:56:19
expired found date
-
created at
2024-10-14 19:56:19
updated at
2024-10-14 19:56:19
length
16
crc
31758
tld
86
nm parts
0
nm random digits
0
nm rare letters
0
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)
deleted subdomains
0
page imported products
0
page imported random
0
page imported parking
0
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 bits
—
server ip
-
mp import status
20
mp rejected date
-
mp saved date
-
mp size orig
20243
mp size raw text
7663
mp inner links count
1
mp inner links status
10 (links queued, awaiting import)
title
XVO: Generalized Visual Odometry via Cross-Modal Self-Training
description
XVO: Generalized Visual Odometry via Cross-Modal Self-Training
image
site name
XVO: Generalized Visual Odometry via Cross-Modal Self-Training
author
updated
2026-02-23 13:53:20
raw text
XVO: Generalized Visual Odometry via Cross-Modal Self-Training XVO: Generalized Visual Odometry via Cross-Modal Self-Training Lei Lai* Zhongkai Shangguan* Jimuyang Zhang Eshed Ohn-Bar Boston University ICCV 2023 Paper arXiv Code Abstract We propose XVO, a semi-supervised learning method for training generalized monocular Visual Odometry (VO) models with robust off-the-self operation across diverse datasets and settings. In contrast to standard monocular VO approaches which often study a known calibration within a single dataset, XVO efficiently learns to recover relative pose with real-world scale from visual scene semantics, i.e., without relying on any known camera parameters. We optimize the motion estimation model via self-training from large amounts of unconstrained and heterogeneous dash camera videos available on YouTube. ...
redirect type
0 (-)
block type
0 (no issues)
detected language
1 (English)
category id
index version
1
spam phrases
0
text nonlatin
0
text cyrillic
0
text characters
5381
text words
904
text unique words
397
text lines
99
text sentences
42
text paragraphs
9
text words per sentence
21
text matched phrases
0
text matched dictionaries
0
links self subdomains
0
links other subdomains
0
links other domains
1
links spam adult
0
links spam random
0
links spam expired
0
links ext activities
2
links ext ecommerce
0
links ext finance
0
links ext crypto
0
links ext booking
0
links ext news
0
links ext leaks
0
links ext ugc
5 - linkedin.com
links ext klim
0
links ext generic
0
dol status
0
dol updated
2026-02-23 13:53:20
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 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
-