8,249 research outputs found
Occlusion Aware Unsupervised Learning of Optical Flow
It has been recently shown that a convolutional neural network can learn
optical flow estimation with unsupervised learning. However, the performance of
the unsupervised methods still has a relatively large gap compared to its
supervised counterpart. Occlusion and large motion are some of the major
factors that limit the current unsupervised learning of optical flow methods.
In this work we introduce a new method which models occlusion explicitly and a
new warping way that facilitates the learning of large motion. Our method shows
promising results on Flying Chairs, MPI-Sintel and KITTI benchmark datasets.
Especially on KITTI dataset where abundant unlabeled samples exist, our
unsupervised method outperforms its counterpart trained with supervised
learning.Comment: CVPR 2018 Camera-read
Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions
Depth estimation is a fundamental problem for light field photography
applications. Numerous methods have been proposed in recent years, which either
focus on crafting cost terms for more robust matching, or on analyzing the
geometry of scene structures embedded in the epipolar-plane images. Significant
improvements have been made in terms of overall depth estimation error;
however, current state-of-the-art methods still show limitations in handling
intricate occluding structures and complex scenes with multiple occlusions. To
address these challenging issues, we propose a very effective depth estimation
framework which focuses on regularizing the initial label confidence map and
edge strength weights. Specifically, we first detect partially occluded
boundary regions (POBR) via superpixel based regularization. Series of
shrinkage/reinforcement operations are then applied on the label confidence map
and edge strength weights over the POBR. We show that after weight
manipulations, even a low-complexity weighted least squares model can produce
much better depth estimation than state-of-the-art methods in terms of average
disparity error rate, occlusion boundary precision-recall rate, and the
preservation of intricate visual features
The Application of Preconditioned Alternating Direction Method of Multipliers in Depth from Focal Stack
Post capture refocusing effect in smartphone cameras is achievable by using
focal stacks. However, the accuracy of this effect is totally dependent on the
combination of the depth layers in the stack. The accuracy of the extended
depth of field effect in this application can be improved significantly by
computing an accurate depth map which has been an open issue for decades. To
tackle this issue, in this paper, a framework is proposed based on
Preconditioned Alternating Direction Method of Multipliers (PADMM) for depth
from the focal stack and synthetic defocus application. In addition to its
ability to provide high structural accuracy and occlusion handling, the
optimization function of the proposed method can, in fact, converge faster and
better than state of the art methods. The evaluation has been done on 21 sets
of focal stacks and the optimization function has been compared against 5 other
methods. Preliminary results indicate that the proposed method has a better
performance in terms of structural accuracy and optimization in comparison to
the current state of the art methods.Comment: 15 pages, 8 figure
Data association and occlusion handling for vision-based people tracking by mobile robots
This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets
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