9,144 research outputs found
Learning to Detect Ground Control Points for Improving the Accuracy of Stereo Matching
International audienceWhile machine learning has been instrumental to the ongoing progress in most areas of computer vision, it has not been applied to the problem of stereo matching with similar frequency or success. We present a supervised learning approach for predicting the correctness of stereo matches based on a random forest and a set of features that capture various forms of information about each pixel.We show highly competitive results in predicting the correctness of matches and in confidence estimation, which allows us to rank pixels according to the reliability of their assigned disparities. Moreover, we show how these confidence values can be used to improve the accuracy of disparity maps by integrating them with an MRF-based stereo algorithm. This is an important distinction from current literature that has mainly focused on sparsification by removing potentially erroneous disparities to generate quasi-dense disparity maps
Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots
Safety is paramount for mobile robotic platforms such as self-driving cars
and unmanned aerial vehicles. This work is devoted to a task that is
indispensable for safety yet was largely overlooked in the past -- detecting
obstacles that are of very thin structures, such as wires, cables and tree
branches. This is a challenging problem, as thin objects can be problematic for
active sensors such as lidar and sonar and even for stereo cameras. In this
work, we propose to use video sequences for thin obstacle detection. We
represent obstacles with edges in the video frames, and reconstruct them in 3D
using efficient edge-based visual odometry techniques. We provide both a
monocular camera solution and a stereo camera solution. The former incorporates
Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter
enjoys a novel, purely vision-based solution. Experiments demonstrated that the
proposed methods are fast and able to detect thin obstacles robustly and
accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio
Entropy-difference based stereo error detection
Stereo depth estimation is error-prone; hence, effective error detection
methods are desirable. Most such existing methods depend on characteristics of
the stereo matching cost curve, making them unduly dependent on functional
details of the matching algorithm. As a remedy, we propose a novel error
detection approach based solely on the input image and its depth map. Our
assumption is that, entropy of any point on an image will be significantly
higher than the entropy of its corresponding point on the image's depth map. In
this paper, we propose a confidence measure, Entropy-Difference (ED) for stereo
depth estimates and a binary classification method to identify incorrect
depths. Experiments on the Middlebury dataset show the effectiveness of our
method. Our proposed stereo confidence measure outperforms 17 existing measures
in all aspects except occlusion detection. Established metrics such as
precision, accuracy, recall, and area-under-curve are used to demonstrate the
effectiveness of our method
Guided Stereo Matching
Stereo is a prominent technique to infer dense depth maps from images, and
deep learning further pushed forward the state-of-the-art, making end-to-end
architectures unrivaled when enough data is available for training. However,
deep networks suffer from significant drops in accuracy when dealing with new
environments. Therefore, in this paper, we introduce Guided Stereo Matching, a
novel paradigm leveraging a small amount of sparse, yet reliable depth
measurements retrieved from an external source enabling to ameliorate this
weakness. The additional sparse cues required by our method can be obtained
with any strategy (e.g., a LiDAR) and used to enhance features linked to
corresponding disparity hypotheses. Our formulation is general and fully
differentiable, thus enabling to exploit the additional sparse inputs in
pre-trained deep stereo networks as well as for training a new instance from
scratch. Extensive experiments on three standard datasets and two
state-of-the-art deep architectures show that even with a small set of sparse
input cues, i) the proposed paradigm enables significant improvements to
pre-trained networks. Moreover, ii) training from scratch notably increases
accuracy and robustness to domain shifts. Finally, iii) it is suited and
effective even with traditional stereo algorithms such as SGM.Comment: CVPR 201
Pushbroom Stereo for High-Speed Navigation in Cluttered Environments
We present a novel stereo vision algorithm that is capable of obstacle
detection on a mobile-CPU processor at 120 frames per second. Our system
performs a subset of standard block-matching stereo processing, searching only
for obstacles at a single depth. By using an onboard IMU and state-estimator,
we can recover the position of obstacles at all other depths, building and
updating a full depth-map at framerate.
Here, we describe both the algorithm and our implementation on a high-speed,
small UAV, flying at over 20 MPH (9 m/s) close to obstacles. The system
requires no external sensing or computation and is, to the best of our
knowledge, the first high-framerate stereo detection system running onboard a
small UAV
Using Self-Contradiction to Learn Confidence Measures in Stereo Vision
Learned confidence measures gain increasing importance for outlier removal
and quality improvement in stereo vision. However, acquiring the necessary
training data is typically a tedious and time consuming task that involves
manual interaction, active sensing devices and/or synthetic scenes. To overcome
this problem, we propose a new, flexible, and scalable way for generating
training data that only requires a set of stereo images as input. The key idea
of our approach is to use different view points for reasoning about
contradictions and consistencies between multiple depth maps generated with the
same stereo algorithm. This enables us to generate a huge amount of training
data in a fully automated manner. Among other experiments, we demonstrate the
potential of our approach by boosting the performance of three learned
confidence measures on the KITTI2012 dataset by simply training them on a vast
amount of automatically generated training data rather than a limited amount of
laser ground truth data.Comment: This paper was accepted to the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), 2016. The copyright was transfered to IEEE
(https://www.ieee.org). The official version of the paper will be made
available on IEEE Xplore (R) (http://ieeexplore.ieee.org). This version of
the paper also contains the supplementary material, which will not appear
IEEE Xplore (R
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