120 research outputs found
Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images
Modeling statistical regularity plays an essential role in ill-posed image
processing problems. Recently, deep learning based methods have been presented
to implicitly learn statistical representation of pixel distributions in
natural images and leverage it as a constraint to facilitate subsequent tasks,
such as color constancy and image dehazing. However, the existing CNN
architecture is prone to variability and diversity of pixel intensity within
and between local regions, which may result in inaccurate statistical
representation. To address this problem, this paper presents a novel fully
point-wise CNN architecture for modeling statistical regularities in natural
images. Specifically, we propose to randomly shuffle the pixels in the origin
images and leverage the shuffled image as input to make CNN more concerned with
the statistical properties. Moreover, since the pixels in the shuffled image
are independent identically distributed, we can replace all the large
convolution kernels in CNN with point-wise () convolution kernels while
maintaining the representation ability. Experimental results on two
applications: color constancy and image dehazing, demonstrate the superiority
of our proposed network over the existing architectures, i.e., using
1/101/100 network parameters and computational cost while achieving
comparable performance.Comment: 9 pages, 7 figures. To appear in ACM MM 201
ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics
Physical simulators have been widely used in robot planning and control.
Among them, differentiable simulators are particularly favored, as they can be
incorporated into gradient-based optimization algorithms that are efficient in
solving inverse problems such as optimal control and motion planning.
Simulating deformable objects is, however, more challenging compared to rigid
body dynamics. The underlying physical laws of deformable objects are more
complex, and the resulting systems have orders of magnitude more degrees of
freedom and therefore they are significantly more computationally expensive to
simulate. Computing gradients with respect to physical design or controller
parameters is typically even more computationally challenging. In this paper,
we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical
simulator for deformable objects, ChainQueen, based on the Moving Least Squares
Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects
including contact and can be seamlessly incorporated into inference, control
and co-design systems. We demonstrate that our simulator achieves high
precision in both forward simulation and backward gradient computation. We have
successfully employed it in a diverse set of control tasks for soft robots,
including problems with nearly 3,000 decision variables.Comment: In submission to ICRA 2019. Supplemental Video:
https://www.youtube.com/watch?v=4IWD4iGIsB4 Project Page:
https://github.com/yuanming-hu/ChainQuee
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