5 research outputs found
LIFT: Learned Invariant Feature Transform
We introduce a novel Deep Network architecture that implements the full
feature point handling pipeline, that is, detection, orientation estimation,
and feature description. While previous works have successfully tackled each
one of these problems individually, we show how to learn to do all three in a
unified manner while preserving end-to-end differentiability. We then
demonstrate that our Deep pipeline outperforms state-of-the-art methods on a
number of benchmark datasets, without the need of retraining.Comment: Accepted to ECCV 2016 (spotlight
Virtual Ground Truth, and Pre-selection of 3D Interest Points for Improved Repeatability Evaluation of 2D Detectors
In Computer Vision, finding simple features is performed using classifiers
called interest point (IP) detectors, which are often utilised to track
features as the scene changes. For 2D based classifiers it has been intuitive
to measure repeated point reliability using 2D metrics given the difficulty to
establish ground truth beyond 2D. The aim is to bridge the gap between 2D
classifiers and 3D environments, and improve performance analysis of 2D IP
classification on 3D objects. This paper builds on existing work with 3D
scanned and artificial models to test conventional 2D feature detectors with
the assistance of virtualised 3D scenes. Virtual space depth is leveraged in
tests to perform pre-selection of closest repeatable points in both 2D and 3D
contexts before repeatability is measured. This more reliable ground truth is
used to analyse testing configurations with a singular and 12 model dataset
across affine transforms in x, y and z rotation, as well as x,y scaling with 9
well known IP detectors. The virtual scene's ground truth demonstrates that 3D
pre-selection eliminates a large portion of false positives that are normally
considered repeated in 2D configurations. The results indicate that 3D virtual
environments can provide assistance in comparing the performance of
conventional detectors when extending their applications to 3D environments,
and can result in better classification of features when testing prospective
classifiers' performance. A ROC based informedness measure also highlights
tradeoffs in 2D/3D performance compared to conventional repeatability measures.Comment: Accepted for publication in CCVPR 2018 Conference Proceedings,
Wellington, New Zealand. 11 pages, 5 figure
Virtual ground truth, and pre-selection of 3D interest points for improved repeatability evaluation of 2D detectors
In Computer Vision, finding simple features is performed using classifiers called interest point (IP) detectors, which are often utilised to track features as the scene changes. For 2D based classifiers it has been intuitive to measure repeated point reliability using 2D metrics given the difficulty to establish ground truth beyond 2D. The aim is to bridge the gap between 2D classifiers and 3D environments, and improve performance analysis of 2D IP classification on 3D objects. This paper builds on existing work with 3D scanned and artificial models to test conventional 2D feature detectors with the assistance of virtualised 3D scenes. Virtual space depth is leveraged in tests to perform pre-selection of closest repeatable points in both 2D and 3D contexts before repeatability is measured. This more reliable ground truth is used to analyse testing configurations with a singular and 12 model dataset across affine transforms in x, y and z rotation, as well as x, y scaling with 9 well known IP detectors. The virtual scene's ground truth demonstrates that 3D preselection eliminates a large portion of false positives that are normally considered repeated in 2D configurations. The results indicate that 3D virtual environments can provide assistance in comparing the performance of conventional detectors when extending their applications to 3D environments, and can result in better classification of features when testing prospective classifiers' performance. A ROC based informedness measure also highlights tradeoffs in 2D/3D performance compared to conventional repeatability measures