# Interest point detectors stability evaluation on ApolloScape dataset

arXiv: https://arxiv.org/abs/1809.11039 · HTML: https://mlsanity.com/paper/1809.11039/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Image Feature Information Extraction for Interest Point Detection: A Review](https://mlsanity.com/paper/2106.07929/) (2021)

## Abstract (arXiv)

In the recent years, a number of novel, deep-learning based, interest point detectors, such as LIFT, DELF, Superpoint or LF-Net was proposed. However there's a lack of a standard benchmark to evaluate suitability of these novel keypoint detectors for real-live applications such as autonomous driving. Traditional benchmarks (e.g. Oxford VGG) are rather limited, as they consist of relatively few images of mostly planar scenes taken in favourable conditions. In this paper we verify if the recent, deep-learning based interest point detectors have the advantage over the traditional, hand-crafted keypoint detectors. To this end, we evaluate stability of a number of hand crafted and recent, learning-based interest point detectors on the street-level view ApolloScape dataset.