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.
All previously mentioned works as well as our work tackle interest point detection. In particular, our goal is to determine succinct interest points at detection time. An alternative approach would be to first extract and describe a large set of interest points, and then reduce them to a minimal set sufficient for localization in post-processing. Several approaches exist where this reduction is based on whether specific features are repeatably detected in multiple images observing the same scene @cite_51 @cite_30 @cite_34 @cite_47 @cite_31 . Interestingly, learning to predict from single images whether already-extracted features are likely to be matched has been already proposed @cite_25 @cite_43 , but in both cases as a filtering step of already-extracted features. Instead, we directly identify (and meaningfully score) succinct interest points in the detector.
@cite_30 @cite_47 @cite_43 @cite_31 @cite_34 @cite_51 @cite_25
2799196020 2155080527 2556970001 2145023731
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. We present a generic detection localization algorithm capable of searching for a visual object of interest without training. The proposed method operates using a single example of an object of interest to find similar matches, does not require prior knowledge (learning) about objects being sought, and does not require any preprocessing step or segmentation of a target image. Our method is based on the computation of local regression kernels as descriptors from a query, which measure the likeness of a pixel to its surroundings. Salient features are extracted from said descriptors and compared against analogous features from the target image. This comparison is done using a matrix generalization of the cosine similarity measure. We illustrate optimality properties of the algorithm using a naive-Bayes framework. The algorithm yields a scalar resemblance map, indicating the likelihood of similarity between the query and all patches in the target image. By employing nonparametric significance tests and nonmaxima suppression, we detect the presence and location of objects similar to the given query. The approach is extended to account for large variations in scale and rotation. High performance is demonstrated on several challenging data sets, indicating successful detection of objects in diverse contexts and under different imaging conditions. Several machine learning tasks require to represent the data using only a sparse set of interest points. An ideal detector is able to find the corresponding interest points even if the data undergo a transformation typical for a given domain. Since the task is of high practical interest in computer vision, many hand-crafted solutions were proposed. In this paper, we ask a fundamental question: can we learn such detectors from scratch? Since it is often unclear what points are interesting, human labelling cannot be used to find a truly unbiased solution. Therefore, the task requires an unsupervised formulation. We are the first to propose such a formulation: training a neural network to rank points in a transformation-invariant manner. Interest points are then extracted from the top bottom quantiles of this ranking. We validate our approach on two tasks: standard RGB image interest point detection and challenging cross-modal interest point detection between RGB and depth images. We quantitatively show that our unsupervised method performs better or on-par with baselines. This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.