Interest point detectors stability evaluation on ApolloScape dataset
This work addresses the need for a standard benchmark to assess keypoint detectors in challenging, real-life scenarios such as autonomous driving, though it is incremental as it applies existing methods to new data.
The paper evaluated the stability of both traditional hand-crafted and recent deep-learning-based interest point detectors on the ApolloScape dataset to determine if the latter have an advantage for real-world applications like autonomous driving.
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.