845 research outputs found
Non-blind Image Restoration Based on Convolutional Neural Network
Blind image restoration processors based on convolutional neural network
(CNN) are intensively researched because of their high performance. However,
they are too sensitive to the perturbation of the degradation model. They
easily fail to restore the image whose degradation model is slightly different
from the trained degradation model. In this paper, we propose a non-blind
CNN-based image restoration processor, aiming to be robust against a
perturbation of the degradation model compared to the blind restoration
processor. Experimental comparisons demonstrate that the proposed non-blind
CNN-based image restoration processor can robustly restore images compared to
existing blind CNN-based image restoration processors.Comment: Accepted by IEEE 7th Global Conference on Consumer Electronics, 201
Researching Customer Satisfaction And Loyalty To Boost Marketing Effectiveness: A Look At Japans Auto Dealerships
Faced with a sluggish economy, car sales have been disappointing in recent years. Given this situation, it is critical that dealerships shift the focus of their sales and marketing activities from attracting new customers to keeping the customers they already have. This move can be expected not only to reduce sales costs, but also contribute to healthy profits
EMR-MSF: Self-Supervised Recurrent Monocular Scene Flow Exploiting Ego-Motion Rigidity
Self-supervised monocular scene flow estimation, aiming to understand both 3D
structures and 3D motions from two temporally consecutive monocular images, has
received increasing attention for its simple and economical sensor setup.
However, the accuracy of current methods suffers from the bottleneck of
less-efficient network architecture and lack of motion rigidity for
regularization. In this paper, we propose a superior model named EMR-MSF by
borrowing the advantages of network architecture design under the scope of
supervised learning. We further impose explicit and robust geometric
constraints with an elaborately constructed ego-motion aggregation module where
a rigidity soft mask is proposed to filter out dynamic regions for stable
ego-motion estimation using static regions. Moreover, we propose a motion
consistency loss along with a mask regularization loss to fully exploit static
regions. Several efficient training strategies are integrated including a
gradient detachment technique and an enhanced view synthesis process for better
performance. Our proposed method outperforms the previous self-supervised works
by a large margin and catches up to the performance of supervised methods. On
the KITTI scene flow benchmark, our approach improves the SF-all metric of the
state-of-the-art self-supervised monocular method by 44% and demonstrates
superior performance across sub-tasks including depth and visual odometry,
amongst other self-supervised single-task or multi-task methods.Comment: To appear at ICCV 202
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph
with respect to a large indoor 3D map. The contributions of this work are
three-fold. First, we develop a new large-scale visual localization method
targeted for indoor environments. The method proceeds along three steps: (i)
efficient retrieval of candidate poses that ensures scalability to large-scale
environments, (ii) pose estimation using dense matching rather than local
features to deal with textureless indoor scenes, and (iii) pose verification by
virtual view synthesis to cope with significant changes in viewpoint, scene
layout, and occluders. Second, we collect a new dataset with reference 6DoF
poses for large-scale indoor localization. Query photographs are captured by
mobile phones at a different time than the reference 3D map, thus presenting a
realistic indoor localization scenario. Third, we demonstrate that our method
significantly outperforms current state-of-the-art indoor localization
approaches on this new challenging data
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