7,033 research outputs found
D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
In this work we address the problem of finding reliable pixel-level
correspondences under difficult imaging conditions. We propose an approach
where a single convolutional neural network plays a dual role: It is
simultaneously a dense feature descriptor and a feature detector. By postponing
the detection to a later stage, the obtained keypoints are more stable than
their traditional counterparts based on early detection of low-level
structures. We show that this model can be trained using pixel correspondences
extracted from readily available large-scale SfM reconstructions, without any
further annotations. The proposed method obtains state-of-the-art performance
on both the difficult Aachen Day-Night localization dataset and the InLoc
indoor localization benchmark, as well as competitive performance on other
benchmarks for image matching and 3D reconstruction.Comment: Accepted at CVPR 201
Hybrid Scene Compression for Visual Localization
Localizing an image wrt. a 3D scene model represents a core task for many
computer vision applications. An increasing number of real-world applications
of visual localization on mobile devices, e.g., Augmented Reality or autonomous
robots such as drones or self-driving cars, demand localization approaches to
minimize storage and bandwidth requirements. Compressing the 3D models used for
localization thus becomes a practical necessity. In this work, we introduce a
new hybrid compression algorithm that uses a given memory limit in a more
effective way. Rather than treating all 3D points equally, it represents a
small set of points with full appearance information and an additional, larger
set of points with compressed information. This enables our approach to obtain
a more complete scene representation without increasing the memory
requirements, leading to a superior performance compared to previous
compression schemes. As part of our contribution, we show how to handle
ambiguous matches arising from point compression during RANSAC. Besides
outperforming previous compression techniques in terms of pose accuracy under
the same memory constraints, our compression scheme itself is also more
efficient. Furthermore, the localization rates and accuracy obtained with our
approach are comparable to state-of-the-art feature-based methods, while using
a small fraction of the memory.Comment: Published at CVPR 201
Proposal Flow: Semantic Correspondences from Object Proposals
Finding image correspondences remains a challenging problem in the presence
of intra-class variations and large changes in scene layout. Semantic flow
methods are designed to handle images depicting different instances of the same
object or scene category. We introduce a novel approach to semantic flow,
dubbed proposal flow, that establishes reliable correspondences using object
proposals. Unlike prevailing semantic flow approaches that operate on pixels or
regularly sampled local regions, proposal flow benefits from the
characteristics of modern object proposals, that exhibit high repeatability at
multiple scales, and can take advantage of both local and geometric consistency
constraints among proposals. We also show that the corresponding sparse
proposal flow can effectively be transformed into a conventional dense flow
field. We introduce two new challenging datasets that can be used to evaluate
both general semantic flow techniques and region-based approaches such as
proposal flow. We use these benchmarks to compare different matching
algorithms, object proposals, and region features within proposal flow, to the
state of the art in semantic flow. This comparison, along with experiments on
standard datasets, demonstrates that proposal flow significantly outperforms
existing semantic flow methods in various settings.Comment: arXiv admin note: text overlap with arXiv:1511.0506
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