Paris-CARLA-3D is a dataset of several dense colored point clouds of outdoor environments built by a mobile LiDAR and camera system. The data are composed of two sets with synthetic data from the open source CARLA simulator (700 million points) and real data acquired in the city of Paris (60 million points), hence the name Paris-CARLA-3D. One of the advantages of this dataset is to have simulated the same LiDAR and camera platform in the open source CARLA simulator as the one used to produce the real data. In addition, manual annotation of the classes using the semantic tags of CARLA was performed on the real data, allowing the testing of transfer methods from the synthetic to the real data. The objective of this dataset is to provide a challenging dataset to evaluate and improve methods on difficult vision tasks for the 3D mapping of outdoor environments: semantic segmentation, instance segmentation, and scene completion. For each task, we describe the evaluation protocol as well as the experiments carried out to establish a baseline.

We present a method for the automatic generation of netlists describing general three-dimensional electrothermal and electromagnetic field problems. Using a pair of structured orthogonal grids as spatial discretisation, a one-to-one correspondence between grid objects and circuit elements is obtained by employing the finite integration technique. The resulting circuit can then be solved with any standard available circuit simulator, alleviating the need for the implementation of a custom time integrator. Additionally, the approach straightforwardly allows for field-circuit coupling simulations by appropriately stamping the circuit description of lumped devices. As the computational domain in wave propagation problems must be finite, stamps representing absorbing boundary conditions are developed as well. Representative numerical examples are used to validate the approach. The results obtained by circuit simulation on the generated netlists are compared with appropriate reference solutions.

No abstract

No abstract

No abstract

No abstract

We present an efficient formulation based on the linear embedding via Green's operators (LEGO) and the eigencurrent expansion method to optimize composite wave interaction structures, e.g., photonic-crystal based devices. In LEGO, a composite structure is broken up into "bricks" that are characterized through scattering operators and the interaction among them is captured using transfer operators. By exploiting this diakoptic nature of LEGO, we show how the optimization is accomplished using an effective operator defined over the bricks (usually few) enclosing the space where the fields are sampled. This operator encompasses the effect of the surrounding and separates the domain to be optimized from the fixed one, enabling us to carry out the optimization with little computational effort.

Abstract:We propose a methodology based on linear embedding via Green's operators (LEGO) and the eigencurrent expansion method (EEM) to efficiently deal with and locally optimize 2-D electrically large electromagnetic band-gap (EBG) structures. In LEGO terminology, the composite EBG structure is broken up (diakopted) into constitutive elements called "bricks" that we characterize through scattering operators by invoking Love's equivalence principle, while, at the same time, the electromagnetic interaction among the bricks is captured by transfer operators. The resulting electromagnetic problem is then succinctly formulated through an integral equation involving the total inverse scattering operator S −1 of the structure. To perform local optimization, the formulation of the problem allows for variations of the electromagnetic properties and the shape of a set of objects in the EBG structure with respect to those of the others, thereby allowing us to tune a compact designated domain within a large one. Finally, the method of moments and the EEM are applied to achieve a considerable reduction in memory use for the overall problem.