4,505 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
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Deep Video Color Propagation
Traditional approaches for color propagation in videos rely on some form of
matching between consecutive video frames. Using appearance descriptors, colors
are then propagated both spatially and temporally. These methods, however, are
computationally expensive and do not take advantage of semantic information of
the scene. In this work we propose a deep learning framework for color
propagation that combines a local strategy, to propagate colors frame-by-frame
ensuring temporal stability, and a global strategy, using semantics for color
propagation within a longer range. Our evaluation shows the superiority of our
strategy over existing video and image color propagation methods as well as
neural photo-realistic style transfer approaches.Comment: BMVC 201
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
Convolutional neural networks have recently demonstrated high-quality
reconstruction for single-image super-resolution. In this paper, we propose the
Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively
reconstruct the sub-band residuals of high-resolution images. At each pyramid
level, our model takes coarse-resolution feature maps as input, predicts the
high-frequency residuals, and uses transposed convolutions for upsampling to
the finer level. Our method does not require the bicubic interpolation as the
pre-processing step and thus dramatically reduces the computational complexity.
We train the proposed LapSRN with deep supervision using a robust Charbonnier
loss function and achieve high-quality reconstruction. Furthermore, our network
generates multi-scale predictions in one feed-forward pass through the
progressive reconstruction, thereby facilitates resource-aware applications.
Extensive quantitative and qualitative evaluations on benchmark datasets show
that the proposed algorithm performs favorably against the state-of-the-art
methods in terms of speed and accuracy.Comment: This work is accepted in CVPR 2017. The code and datasets are
available on http://vllab.ucmerced.edu/wlai24/LapSRN
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