Transformation Optics and the Geometry of Light

Metamaterials are beginning to transform optics and microwave technology thanks to their versatile properties that, in many cases, can be tailored according to practical needs and desires. Although metamaterials are surely not the answer to all engineering problems, they have inspired a series of significant technological developments and also some imaginative research, because they invite researchers and inventors to dream. Imagine there were no practical limits on the electromagnetic properties of materials. What is possible? And what is not? If there are no practical limits, what are the fundamental limits? Such questions inspire taking a fresh look at the foundations of optics and at connections between optics and other areas of physics. In this article we discuss such a connection, the relationship between optics and general relativity, or, expressed more precisely, between geometrical ideas normally applied in general relativity and the propagation of light, or electromagnetic waves in general, in materials. We also discuss how this connection is applied: in invisibility devices, perfect lenses, the optical Aharonov-Bohm effect of vortices and in analogues of the event horizon.Comment: 72 pages, 18 figures, preprint with low-resolution images. Introduction to transformation optics, to appear in Progress in Optics (edited by Emil Wolf

Fast soft-tissue deformations with FEM

Soft body simulation has been a very active research area in computer animation since Baraff and Witkin's 1998 work on cloth simulation, which led Pixar to start using such techniques in all of its animated movies that followed. Many challenges in these simulations come from different roots. From a numerical point of view, deformable systems are large sparse problems that can become numerically unstable at surprising rates and may need to be modified at each time-step. From a mathematical point of view, hyperelastic models defined by continuum mechanics need to be derived, established and configured. And from the geometric side, physical interaction with the environment and self-collisions may need to be detected and introduced into the solver. It is a fact that the Computer Graphics academia primarily focuses on offline methods, both for rendering and simulation. At the same time, the advances from the industry mainly apply to real-time rendering. However, we wondered how such high-quality simulation methods would map to a real-time use case. In this thesis, we delve into the simulation system used by Pixar's Fizt2 simulator, based on the Finite Element Method, and investigate how to apply the same techniques in real-time while preserving robustness and fidelity, altogether providing the user with some interaction mechanisms. A 3D engine for simulating deformable materials has been developed following the described models, with an interactive interface that allows the definition and configuration of scenes and later interaction with the simulation

Spatially Adaptive Computation Time for Residual Networks

This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image. This architecture is end-to-end trainable, deterministic and problem-agnostic. It is therefore applicable without any modifications to a wide range of computer vision problems such as image classification, object detection and image segmentation. We present experimental results showing that this model improves the computational efficiency of Residual Networks on the challenging ImageNet classification and COCO object detection datasets. Additionally, we evaluate the computation time maps on the visual saliency dataset cat2000 and find that they correlate surprisingly well with human eye fixation positions.Comment: CVPR 201

DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse points

Multi-view stereo (MVS) is the golden mean between the accuracy of active depth sensing and the practicality of monocular depth estimation. Cost volume based approaches employing 3D convolutional neural networks (CNNs) have considerably improved the accuracy of MVS systems. However, this accuracy comes at a high computational cost which impedes practical adoption. Distinct from cost volume approaches, we propose an efficient depth estimation approach by first (a) detecting and evaluating descriptors for interest points, then (b) learning to match and triangulate a small set of interest points, and finally (c) densifying this sparse set of 3D points using CNNs. An end-to-end network efficiently performs all three steps within a deep learning framework and trained with intermediate 2D image and 3D geometric supervision, along with depth supervision. Crucially, our first step complements pose estimation using interest point detection and descriptor learning. We demonstrate state-of-the-art results on depth estimation with lower compute for different scene lengths. Furthermore, our method generalizes to newer environments and the descriptors output by our network compare favorably to strong baselines. Code is available at https://github.com/magicleap/DELTASComment: ECCV 202