14 research outputs found
Shape and Illumination from Shading Using the Generic Viewpoint Assumption
The Generic Viewpoint Assumption (GVA) states that the position of the viewer or the light in a scene is not special. Thus, any estimated parameters from an observation should be stable under small perturbations such as object, viewpoint or light positions. The GVA has been analyzed and quantified in previous works, but has not been put to practical use in actual vision tasks. In this paper, we show how to utilize the GVA to estimate shape and illumination from a single shading image, without the use of other priors. We propose a novel linearized Spherical Harmonics (SH) shading model which enables us to obtain a computationally efficient form of the GVA term. Together with a data term, we build a model whose unknowns are shape and SH illumination. The model parameters are estimated using the Alternating Direction Method of Multipliers embedded in a multi-scale estimation framework. In this prior-free framework, we obtain competitive shape and illumination estimation results under a variety of models and lighting conditions, requiring fewer assumptions than competing methods.National Science Foundation (U.S.). Directorate for Computer and Information Science and Engineering/Division of Information & Intelligent Systems (Award 1212928)Qatar Computing Research Institut
Parallel Optimal Control for Cooperative Automation of Large-scale Connected Vehicles via ADMM
This paper proposes a parallel optimization algorithm for cooperative
automation of large-scale connected vehicles. The task of cooperative
automation is formulated as a centralized optimization problem taking the whole
decision space of all vehicles into account. Considering the uncertainty of the
environment, the problem is solved in a receding horizon fashion. Then, we
employ the alternating direction method of multipliers (ADMM) to solve the
centralized optimization in a parallel way, which scales more favorably to
large-scale instances. Also, Taylor series is used to linearize nonconvex
constraints caused by coupling collision avoidance constraints among
interactive vehicles. Simulations with two typical traffic scenes for multiple
vehicles demonstrate the effectiveness and efficiency of our method
Proximal operators for multi-agent path planning
We address the problem of planning collision-free paths for multiple agents
using optimization methods known as proximal algorithms. Recently this approach
was explored in Bento et al. 2013, which demonstrated its ease of
parallelization and decentralization, the speed with which the algorithms
generate good quality solutions, and its ability to incorporate different
proximal operators, each ensuring that paths satisfy a desired property.
Unfortunately, the operators derived only apply to paths in 2D and require that
any intermediate waypoints we might want agents to follow be preassigned to
specific agents, limiting their range of applicability. In this paper we
resolve these limitations. We introduce new operators to deal with agents
moving in arbitrary dimensions that are faster to compute than their 2D
predecessors and we introduce landmarks, space-time positions that are
automatically assigned to the set of agents under different optimality
criteria. Finally, we report the performance of the new operators in several
numerical experiments.Comment: See movie at http://youtu.be/gRnsjd_ocx