Detecting interest points is a key component of vision-based estimation algorithms, such as visual odometry or visual SLAM. Classically, interest point detection has been done with methods such as Harris, FAST, or DoG. Recently, better detectors have been proposed based on Neural Networks. Traditionally, interest point detectors have been designed to maximize repeatability or matching score. Instead, we pursue another metric, which we call succinctness. This metric captures the minimum amount of interest points that need to be extracted in order to achieve accurate relative pose estimation. Extracting a minimum amount of interest points is attractive for many applications, because it reduces computational load, memory, and, potentially, data transmission. We propose a novel reinforcement- and ranking-based training framework, which uses a full relative pose estimation pipeline during training. It can be trained in an unsupervised manner, without pose or 3D point ground truth. Using this training framework, we present a detector which outperforms previous interest point detectors in terms of succinctness on a variety of publicly available datasets.
The problem with relying on distinctiveness as interest point selection criterion is that it does not necessarily result in high repeatability, unless all distinctive points are selected. A more refined interest point selection criterion is needed if one wants to preserve repeatability when extracting less points. Some of the aforementioned works have attempted to derive such a criterion based on models or heuristics. An alternative and more promising method to deriving this criterion is data-driven, using machine learning. An early method based on a neural network has been proposed in @cite_33 , though it was limited to three layers due to the computational constraints of the time, and also only applied to the edge regions of an image. Subsequently, @cite_16 @cite_7 @cite_39 used other types of regression functions to learn detectors.
@cite_16 @cite_33 @cite_7 @cite_39
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We present a method for learning efficient feature detectors based on in-situ evaluation as an alternative to hand-engineered feature detection methods. We demonstrate our in-situ learning approach by developing a feature detector optimized for stereo visual odometry. Our feature detector parameterization is that of a convolutional filter. We show that feature detectors competitive with the best hand-designed alternatives can be learned by random sampling in the space of convolutional filters and we provide a way to bias the search toward regions of the search space that produce effective results. Further, we describe our approach for obtaining the ground-truth data needed by our learning system in real, everyday environments. Existing corner detection methods either extract boundaries and search for points having maximum curvature or apply a local operator in parallel to neighborhoods of a gray level picture. The key problem in these methods is the conversion of the gray levels of a pixel into a value reflecting a property of cornerness at that point. A neural network's ability to learn and to adapt together with its inherent parallelism and robustness has made it a natural choice for machine vision applications. This paper presents the application of neural networks to the problem of detecting corners in 2-D images. The performance of the system suggests its robustness and great potential. The performance of high-level computer vision applications is tightly coupled with the low-level vision operations that are commonly required. Thus, it is advantageous to have low-level feature extractors that are optimal with respect to a desired performance criteria. This paper presents a novel approach that uses Genetic Programming as a learning framework that generates a specific type of low-level feature extractor: Interest Point Detector. The learning process is posed as an optimization problem. The optimization criterion is designed to promote the emergence of the detectors geometric stability under different types of image transformations and global separability between detected points. This concept is represented by the operators repeatability rate [11]. Results prove that our approach is effective at automatically generating low-level feature extractors. This paper presents two different evolved operators: IPGP1 and IPGP2. Their performance is comparable with the Harris [5] operator given their excellent repeatability rate. Furthermore, the learning process was able to rediscover the DET corner detector proposed by Beaudet. We introduce a learning-based approach to detect repeatable keypoints under drastic imaging changes of weather and lighting conditions to which state-of-the-art keypoint detectors are surprisingly sensitive. We first identify good keypoint candidates in multiple training images taken from the same viewpoint. We then train a regressor to predict a score map whose maxima are those points so that they can be found by simple non-maximum suppression. As there are no standard datasets to test the influence of these kinds of changes, we created our own, which we will make publicly available. We will show that our method significantly outperforms the state-of-the-art methods in such challenging conditions, while still achieving state-of-the-art performance on untrained standard datasets.