178 research outputs found
Neighbourhood Consensus Networks
We address the problem of finding reliable dense correspondences between a
pair of images. This is a challenging task due to strong appearance differences
between the corresponding scene elements and ambiguities generated by
repetitive patterns. The contributions of this work are threefold. First,
inspired by the classic idea of disambiguating feature matches using semi-local
constraints, we develop an end-to-end trainable convolutional neural network
architecture that identifies sets of spatially consistent matches by analyzing
neighbourhood consensus patterns in the 4D space of all possible
correspondences between a pair of images without the need for a global
geometric model. Second, we demonstrate that the model can be trained
effectively from weak supervision in the form of matching and non-matching
image pairs without the need for costly manual annotation of point to point
correspondences. Third, we show the proposed neighbourhood consensus network
can be applied to a range of matching tasks including both category- and
instance-level matching, obtaining the state-of-the-art results on the PF
Pascal dataset and the InLoc indoor visual localization benchmark.Comment: In Proceedings of the 32nd Conference on Neural Information
Processing Systems (NeurIPS 2018
Real-Time 6DOF Pose Relocalization for Event Cameras with Stacked Spatial LSTM Networks
We present a new method to relocalize the 6DOF pose of an event camera solely
based on the event stream. Our method first creates the event image from a list
of events that occurs in a very short time interval, then a Stacked Spatial
LSTM Network (SP-LSTM) is used to learn the camera pose. Our SP-LSTM is
composed of a CNN to learn deep features from the event images and a stack of
LSTM to learn spatial dependencies in the image feature space. We show that the
spatial dependency plays an important role in the relocalization task and the
SP-LSTM can effectively learn this information. The experimental results on a
publicly available dataset show that our approach generalizes well and
outperforms recent methods by a substantial margin. Overall, our proposed
method reduces by approx. 6 times the position error and 3 times the
orientation error compared to the current state of the art. The source code and
trained models will be released.Comment: 7 pages, 5 figure
Dual-Resolution Correspondence Networks
We tackle the problem of establishing dense pixel-wise correspondences
between a pair of images. In this work, we introduce Dual-Resolution
Correspondence Networks (DRC-Net), to obtain pixel-wise correspondences in a
coarse-to-fine manner. DRC-Net extracts both coarse- and fine- resolution
feature maps. The coarse maps are used to produce a full but coarse 4D
correlation tensor, which is then refined by a learnable neighbourhood
consensus module. The fine-resolution feature maps are used to obtain the final
dense correspondences guided by the refined coarse 4D correlation tensor. The
selected coarse-resolution matching scores allow the fine-resolution features
to focus only on a limited number of possible matches with high confidence. In
this way, DRC-Net dramatically increases matching reliability and localisation
accuracy, while avoiding to apply the expensive 4D convolution kernels on
fine-resolution feature maps. We comprehensively evaluate our method on
large-scale public benchmarks including HPatches, InLoc, and Aachen Day-Night.
It achieves the state-of-the-art results on all of them
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