2 research outputs found
Towards Keypoint Guided Self-Supervised Depth Estimation
This paper proposes to use keypoints as a self-supervision clue for learning
depth map estimation from a collection of input images. As ground truth depth
from real images is difficult to obtain, there are many unsupervised and
self-supervised approaches to depth estimation that have been proposed. Most of
these unsupervised approaches use depth map and ego-motion estimations to
reproject the pixels from the current image into the adjacent image from the
image collection. Depth and ego-motion estimations are evaluated based on pixel
intensity differences between the correspondent original and reprojected
pixels. Instead of reprojecting the individual pixels, we propose to first
select image keypoints in both images and then reproject and compare the
correspondent keypoints of the two images. The keypoints should describe the
distinctive image features well. By learning a deep model with and without the
keypoint extraction technique, we show that using the keypoints improve the
depth estimation learning. We also propose some future directions for
keypoint-guided learning of structure-from-motion problems
On the Comparison of Classic and Deep Keypoint Detector and Descriptor Methods
The purpose of this study is to give a performance comparison between several
classic hand-crafted and deep key-point detector and descriptor methods. In
particular, we consider the following classical algorithms: SIFT, SURF, ORB,
FAST, BRISK, MSER, HARRIS, KAZE, AKAZE, AGAST, GFTT, FREAK, BRIEF and RootSIFT,
where a subset of all combinations is paired into detector-descriptor
pipelines. Additionally, we analyze the performance of two recent and
perspective deep detector-descriptor models, LF-Net and SuperPoint. Our
benchmark relies on the HPSequences dataset that provides real and diverse
images under various geometric and illumination changes. We analyze the
performance on three evaluation tasks: keypoint verification, image matching
and keypoint retrieval. The results show that certain classic and deep
approaches are still comparable, with some classic detector-descriptor
combinations overperforming pretrained deep models. In terms of the execution
times of tested implementations, SuperPoint model is the fastest, followed by
ORB. The source code is published on
\url{https://github.com/kristijanbartol/keypoint-algorithms-benchmark}