Anchors
| Anchor text | Ref. domains ▾ | Top DR | Ref. pages | Links to target | Dofollow links |
|---|---|---|---|---|---|
| Andreas Geiger | 13 | — | 0 | 13 | 12 92.3% |
| KITTI | 8 | — | 0 | 8 | 7 87.5% |
| http://www.cvlibs.net/datasets/kitti/ | 5 | — | 0 | 6 | 6 100% |
| here | 2 | — | 0 | 2 | 1 50% |
| http://www.cvlibs.net/datasets/kitti | 2 | — | 0 | 3 | 3 100% |
| The KITTI Vision Benchmark Suite | 2 | — | 0 | 3 | 3 100% |
| supplementary | 2 | — | 0 | 2 | 2 100% |
| 2 | — | 0 | 7 | 7 100% | |
| KITTI format | 2 | — | 0 | 5 | 4 80% |
| Prof. Andreas Geiger | 2 | — | 0 | 2 | 2 100% |
| Website | 1 | — | 0 | 1 | 1 100% |
| LIBVISO2: C++ Library for Visual Odometry 2 | 1 | — | 0 | 1 | 1 100% |
| www.cvlibs.net/datasets/kitti | 1 | — | 0 | 1 | 1 100% |
| KITTI Dataset | 1 | — | 0 | 1 | 1 100% |
| KITTI オブジェクト検出データセット | 1 | — | 0 | 1 | 0 0% |
| Geiger | 1 | — | 0 | 2 | 2 100% |
| 1 | — | 0 | 14 | 14 100% | |
| KITTI-360 dataset | 1 | — | 0 | 1 | 1 100% |
| KITTI VISION BENCHMARK SUITE | 1 | — | 0 | 1 | 1 100% |
| KITTI Depth Prediction benchmark | 1 | — | 0 | 1 | 1 100% |
| KITTI 多对象追踪和分割 | 1 | — | 0 | 1 | 1 100% |
| paper | 1 | — | 0 | 1 | 1 100% |
| Green Version | 1 | — | 0 | 1 | 1 100% |
| www.cvlibs.net/projects/objectsceneflow | 1 | — | 0 | 1 | 1 100% |
| Project page | 1 | — | 0 | 1 | 1 100% |
| KITTI 2012 | 1 | — | 0 | 1 | 0 0% |
| KITTI Odometry Benchmark | 1 | — | 0 | 1 | 1 100% |
| https://www.cvlibs.net/datasets/kitti/eval_odometry.php | 1 | — | 0 | 1 | 1 100% |
| suppmat | 1 | — | 0 | 10 | 10 100% |
| http://www.cvlibs.net/datasets/kitti/raw_data.php | 1 | — | 0 | 1 | 1 100% |
| KITTI data | 1 | — | 0 | 1 | 0 0% |
| Publications | 1 | — | 0 | 1 | 1 100% |
| KITTI 3D Object detection | 1 | — | 0 | 1 | 0 0% |
| DiscreteFlow | 1 | — | 0 | 10948 | 10948 100% |
| project | 1 | — | 0 | 1 | 1 100% |
| KITTI 3D object detection benchmark | 1 | — | 0 | 1 | 1 100% |
| KITTI Cars | 1 | — | 0 | 1 | 1 100% |
| libelas | 1 | — | 0 | 1 | 1 100% |
| Supplementary Material | 1 | — | 0 | 1 | 1 100% |
| Slides | 1 | — | 0 | 1 | 1 100% |
| the KITTI Vision Benchmark | 1 | — | 0 | 1 | 1 100% |
| KITTI dataset | 1 | — | 0 | 1 | 1 100% |
| KITTI Vision Benchmark Suite | 1 | — | 0 | 1 | 1 100% |
| KITTI榜单 | 1 | — | 0 | 1 | 1 100% |
| Quiz Server / Chat | 1 | — | 0 | 1 | 1 100% |
| Projected GAN | 1 | — | 0 | 1 | 1 100% |
| KITTI 深度预测 | 1 | — | 0 | 1 | 1 100% |
| Paper | 1 | — | 0 | 2 | 2 100% |
| http://www.cvlibs.net/datasets/kitti/eval\_tracking.php | 1 | — | 0 | 1 | 1 100% |
| best in non-stereo setting | 1 | — | 0 | 1 | 1 100% |
Frequently Asked Questions
What anchor texts are used to link to cvlibs.net?
This page shows all anchor texts found in backlinks pointing to cvlibs.net, sorted by the number of referring domains using each anchor. Anchor texts range from branded terms (like the domain name itself) to keyword-rich phrases that describe the linked content. The distribution of anchor texts reveals how other websites perceive and describe cvlibs.net.
What is anchor text?
Anchor text is the visible, clickable text in a hyperlink. Search engines use anchor text as a signal to understand what the linked page is about. For example, if many sites link to a page using the anchor text "best running shoes," search engines infer that the page is relevant to that topic. Anchor text appears in several forms: exact-match (contains target keywords), branded (uses the company or domain name), generic (like "click here"), and naked URLs.
Why is anchor text analysis important for SEO?
Anchor text analysis helps identify potential SEO risks and opportunities. A natural backlink profile has diverse anchor texts including branded terms, generic phrases, and topic-relevant keywords. Over-optimization, where too many backlinks use the same exact-match keyword anchor, can trigger search engine penalties. Conversely, understanding which anchors drive the most authority (measured by referring domain count and DR) helps prioritize link building efforts.
How many unique anchor texts does cvlibs.net have?
The anchor text report for cvlibs.net displays all distinct anchor texts grouped by their hash. Each row shows how many unique referring domains use that anchor, the total number of links, and the dofollow percentage. A high number of unique anchors generally indicates a healthy, natural backlink profile with diverse link sources.