74 research outputs found
RAFT: Recurrent All-Pairs Field Transforms for Optical Flow
We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network
architecture for optical flow. RAFT extracts per-pixel features, builds
multi-scale 4D correlation volumes for all pairs of pixels, and iteratively
updates a flow field through a recurrent unit that performs lookups on the
correlation volumes. RAFT achieves state-of-the-art performance. On KITTI, RAFT
achieves an F1-all error of 5.10%, a 16% error reduction from the best
published result (6.10%). On Sintel (final pass), RAFT obtains an
end-point-error of 2.855 pixels, a 30% error reduction from the best published
result (4.098 pixels). In addition, RAFT has strong cross-dataset
generalization as well as high efficiency in inference time, training speed,
and parameter count. Code is available at https://github.com/princeton-vl/RAFT.Comment: fixed a formatting issue, Eq 7. no change in conten
Learning Stereo from Single Images
Supervised deep networks are among the best methods for finding
correspondences in stereo image pairs. Like all supervised approaches, these
networks require ground truth data during training. However, collecting large
quantities of accurate dense correspondence data is very challenging. We
propose that it is unnecessary to have such a high reliance on ground truth
depths or even corresponding stereo pairs. Inspired by recent progress in
monocular depth estimation, we generate plausible disparity maps from single
images. In turn, we use those flawed disparity maps in a carefully designed
pipeline to generate stereo training pairs. Training in this manner makes it
possible to convert any collection of single RGB images into stereo training
data. This results in a significant reduction in human effort, with no need to
collect real depths or to hand-design synthetic data. We can consequently train
a stereo matching network from scratch on datasets like COCO, which were
previously hard to exploit for stereo. Through extensive experiments we show
that our approach outperforms stereo networks trained with standard synthetic
datasets, when evaluated on KITTI, ETH3D, and Middlebury.Comment: Accepted as an oral presentation at ECCV 202
Active stereo platform: online epipolar geometry update
This paper presents a novel method to update a variable epipolar geometry platform directly from the motor encoder based on mapping the motor encoder angle to the image space angle, avoiding the use of feature detection algorithms. First, an offline calibration is performed to establish a relationship between the image space and the hardware space. Second, a transformation matrix is generated using the results from this mapping. The transformation matrix uses the updated epipolar geometry of the platform to rectify the images for further processing. The system has an overall error in the projection of ± 5 pixels, which drops to ± 1.24 pixels when the verge angle increases beyond 10°. The platform used in this project has 3° of freedom to control the verge angle and the size of the baseline
Semi-Dense 3D Reconstruction with a Stereo Event Camera
Event cameras are bio-inspired sensors that offer several advantages, such as
low latency, high-speed and high dynamic range, to tackle challenging scenarios
in computer vision. This paper presents a solution to the problem of 3D
reconstruction from data captured by a stereo event-camera rig moving in a
static scene, such as in the context of stereo Simultaneous Localization and
Mapping. The proposed method consists of the optimization of an energy function
designed to exploit small-baseline spatio-temporal consistency of events
triggered across both stereo image planes. To improve the density of the
reconstruction and to reduce the uncertainty of the estimation, a probabilistic
depth-fusion strategy is also developed. The resulting method has no special
requirements on either the motion of the stereo event-camera rig or on prior
knowledge about the scene. Experiments demonstrate our method can deal with
both texture-rich scenes as well as sparse scenes, outperforming
state-of-the-art stereo methods based on event data image representations.Comment: 19 pages, 8 figures, Video: https://youtu.be/Qrnpj2FD1e
Fuzzy Free Path Detection from Disparity Maps by Using Least-Squares Fitting to a Plane
A method to detect obstacle-free paths in real-time which works as part of a cognitive navigation aid system for visually impaired people is proposed. It is based on the analysis of disparity maps obtained from a stereo vision system which is carried by the blind user. The presented detection method consists of a fuzzy logic system that assigns a certainty to be part of a free path to each group of pixels, depending on the parameters of a planar-model fitting. We also present experimental results on different real outdoor scenarios showing that our method is the most reliable in the sense that it minimizes the false positives rate.N. Ortigosa acknowledges the support of Universidad Politecnica de Valencia under grant FPI-UPV 2008 and Spanish Ministry of Science and Innovation under grant MTM2010-15200. S. Morillas acknowledges the support of Universidad Politecnica de Valencia under grant PAID-05-12-SP20120696.Ortigosa Araque, N.; Morillas Gómez, S. (2014). 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Real-time correllation - based stereo vision with reduced error borders
The research reported here describes an approach to real-time stereo vision using non specialised computer equipment. In particular the new methods improve standard correlation considerably. The extensive results presented in terms of performance allow us to use simple cameras to perform excellent image analysis. This work has been used and extended by one of the authors in real applications for mobile robots. The paper appeared in one of the leading journals in Computer Vision with an impact factor of 6.085
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