7 research outputs found
Large scale evaluation of local image feature detectors on homography datasets
We present a large scale benchmark for the evaluation of local feature
detectors. Our key innovation is the introduction of a new evaluation protocol
which extends and improves the standard detection repeatability measure. The
new protocol is better for assessment on a large number of images and reduces
the dependency of the results on unwanted distractors such as the number of
detected features and the feature magnification factor. Additionally, our
protocol provides a comprehensive assessment of the expected performance of
detectors under several practical scenarios. Using images from the
recently-introduced HPatches dataset, we evaluate a range of state-of-the-art
local feature detectors on two main tasks: viewpoint and illumination invariant
detection. Contrary to previous detector evaluations, our study contains an
order of magnitude more image sequences, resulting in a quantitative evaluation
significantly more robust to over-fitting. We also show that traditional
detectors are still very competitive when compared to recent deep-learning
alternatives.Comment: Accepted to BMVC 201
HPatches: A benchmark and evaluation of handcrafted and learned local descriptors
In this paper, we propose a novel benchmark for evaluating local image
descriptors. We demonstrate that the existing datasets and evaluation protocols
do not specify unambiguously all aspects of evaluation, leading to ambiguities
and inconsistencies in results reported in the literature. Furthermore, these
datasets are nearly saturated due to the recent improvements in local
descriptors obtained by learning them from large annotated datasets. Therefore,
we introduce a new large dataset suitable for training and testing modern
descriptors, together with strictly defined evaluation protocols in several
tasks such as matching, retrieval and classification. This allows for more
realistic, and thus more reliable comparisons in different application
scenarios. We evaluate the performance of several state-of-the-art descriptors
and analyse their properties. We show that a simple normalisation of
traditional hand-crafted descriptors can boost their performance to the level
of deep learning based descriptors within a realistic benchmarks evaluation
Single-Image Depth Prediction Makes Feature Matching Easier
Good local features improve the robustness of many 3D re-localization and
multi-view reconstruction pipelines. The problem is that viewing angle and
distance severely impact the recognizability of a local feature. Attempts to
improve appearance invariance by choosing better local feature points or by
leveraging outside information, have come with pre-requisites that made some of
them impractical. In this paper, we propose a surprisingly effective
enhancement to local feature extraction, which improves matching. We show that
CNN-based depths inferred from single RGB images are quite helpful, despite
their flaws. They allow us to pre-warp images and rectify perspective
distortions, to significantly enhance SIFT and BRISK features, enabling more
good matches, even when cameras are looking at the same scene but in opposite
directions.Comment: 14 pages, 7 figures, accepted for publication at the European
conference on computer vision (ECCV) 202
High-Resolution Feature Evaluation Benchmark
Abstract. Benchmark data sets consisting of image pairs and ground truth homographies are used for evaluating fundamental computer vision challenges, such as the detection of image features. The mostly used benchmark provides data with only low resolution images. This paper presents an evaluation benchmark consisting of high resolution images of up to 8 megapixels and highly accurate homographies. State of the art feature detection approaches are evaluated using the new benchmark data. It is shown that existing approaches perform differently on the high resolution data compared to the same images with lower resolution.