5,506 research outputs found
Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation
The monocular vision-based simultaneous localization and mapping (vSLAM) is
one of the most challenging problem in mobile robotics and computer vision. In
this work we study the post-processing techniques applied to sparse 3D
point-cloud maps, obtained by feature-based vSLAM algorithms. Map
post-processing is split into 2 major steps: 1) noise and outlier removal and
2) upsampling. We evaluate different combinations of known algorithms for
outlier removing and upsampling on datasets of real indoor and outdoor
environments and identify the most promising combination. We further use it to
convert a point-cloud map, obtained by the real UAV performing indoor flight to
3D voxel grid (octo-map) potentially suitable for path planning.Comment: 10 pages, 4 figures, camera-ready version of paper for "The 3rd
International Conference on Interactive Collaborative Robotics (ICR 2018)
End-to-End Learning of Video Super-Resolution with Motion Compensation
Learning approaches have shown great success in the task of super-resolving
an image given a low resolution input. Video super-resolution aims for
exploiting additionally the information from multiple images. Typically, the
images are related via optical flow and consecutive image warping. In this
paper, we provide an end-to-end video super-resolution network that, in
contrast to previous works, includes the estimation of optical flow in the
overall network architecture. We analyze the usage of optical flow for video
super-resolution and find that common off-the-shelf image warping does not
allow video super-resolution to benefit much from optical flow. We rather
propose an operation for motion compensation that performs warping from low to
high resolution directly. We show that with this network configuration, video
super-resolution can benefit from optical flow and we obtain state-of-the-art
results on the popular test sets. We also show that the processing of whole
images rather than independent patches is responsible for a large increase in
accuracy.Comment: Accepted to GCPR201
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
A Fully Progressive Approach to Single-Image Super-Resolution
Recent deep learning approaches to single image super-resolution have
achieved impressive results in terms of traditional error measures and
perceptual quality. However, in each case it remains challenging to achieve
high quality results for large upsampling factors. To this end, we propose a
method (ProSR) that is progressive both in architecture and training: the
network upsamples an image in intermediate steps, while the learning process is
organized from easy to hard, as is done in curriculum learning. To obtain more
photorealistic results, we design a generative adversarial network (GAN), named
ProGanSR, that follows the same progressive multi-scale design principle. This
not only allows to scale well to high upsampling factors (e.g., 8x) but
constitutes a principled multi-scale approach that increases the reconstruction
quality for all upsampling factors simultaneously. In particular ProSR ranks
2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge
[34]. Compared to the top-ranking team, our model is marginally lower, but runs
5 times faster
Video Propagation Networks
We propose a technique that propagates information forward through video
data. The method is conceptually simple and can be applied to tasks that
require the propagation of structured information, such as semantic labels,
based on video content. We propose a 'Video Propagation Network' that processes
video frames in an adaptive manner. The model is applied online: it propagates
information forward without the need to access future frames. In particular we
combine two components, a temporal bilateral network for dense and video
adaptive filtering, followed by a spatial network to refine features and
increased flexibility. We present experiments on video object segmentation and
semantic video segmentation and show increased performance comparing to the
best previous task-specific methods, while having favorable runtime.
Additionally we demonstrate our approach on an example regression task of color
propagation in a grayscale video.Comment: Appearing in Computer Vision and Pattern Recognition, 2017 (CVPR'17
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