Path-tracing Monte Carlo Library for 3D Radiative Transfer in Highly Resolved Cloudy Atmospheres

Interactions between clouds and radiation are at the root of many difficulties in numerically predicting future weather and climate and in retrieving the state of the atmosphere from remote sensing observations. The large range of issues related to these interactions, and in particular to three-dimensional interactions, motivated the development of accurate radiative tools able to compute all types of radiative metrics, from monochromatic, local and directional observables, to integrated energetic quantities. In the continuity of this community effort, we propose here an open-source library for general use in Monte Carlo algorithms. This library is devoted to the acceleration of path-tracing in complex data, typically high-resolution large-domain grounds and clouds. The main algorithmic advances embedded in the library are those related to the construction and traversal of hierarchical grids accelerating the tracing of paths through heterogeneous fields in null-collision (maximum cross-section) algorithms. We show that with these hierarchical grids, the computing time is only weakly sensitivive to the refinement of the volumetric data. The library is tested with a rendering algorithm that produces synthetic images of cloud radiances. Two other examples are given as illustrations, that are respectively used to analyse the transmission of solar radiation under a cloud together with its sensitivity to an optical parameter, and to assess a parametrization of 3D radiative effects of clouds.Comment: Submitted to JAMES, revised and submitted again (this is v2

Weakly supervised 3D Reconstruction with Adversarial Constraint

Supervised 3D reconstruction has witnessed a significant progress through the use of deep neural networks. However, this increase in performance requires large scale annotations of 2D/3D data. In this paper, we explore inexpensive 2D supervision as an alternative for expensive 3D CAD annotation. Specifically, we use foreground masks as weak supervision through a raytrace pooling layer that enables perspective projection and backpropagation. Additionally, since the 3D reconstruction from masks is an ill posed problem, we propose to constrain the 3D reconstruction to the manifold of unlabeled realistic 3D shapes that match mask observations. We demonstrate that learning a log-barrier solution to this constrained optimization problem resembles the GAN objective, enabling the use of existing tools for training GANs. We evaluate and analyze the manifold constrained reconstruction on various datasets for single and multi-view reconstruction of both synthetic and real images

Counterexample-Guided Data Augmentation

We present a novel framework for augmenting data sets for machine learning based on counterexamples. Counterexamples are misclassified examples that have important properties for retraining and improving the model. Key components of our framework include a counterexample generator, which produces data items that are misclassified by the model and error tables, a novel data structure that stores information pertaining to misclassifications. Error tables can be used to explain the model's vulnerabilities and are used to efficiently generate counterexamples for augmentation. We show the efficacy of the proposed framework by comparing it to classical augmentation techniques on a case study of object detection in autonomous driving based on deep neural networks

Sensormodelle zur Simulation der Umfelderfassung für Systeme des automatisierten Fahrens

The use of sensor models allows the simulation of environmental perception in automated driving systems, aiding in development and testing efforts. This work systematically discusses the different types of sensor models and introduces an architecture for statistics based as well as for physically motivated sensor models. Each approach is grounded in real world observations of sensor measurements and is designed for portability and the ease of further extensions.Die Nutzung von Sensormodellen für die Umfelderfassung ebnet den Weg für die simulationsgestützte Entwicklung von Systemen des automatisierten Fahrens. In dieser Arbeit wird eine Systematik für verschiedene Arten von Sensormodellen eingeführt und eine Umsetzung von statistischen sowie von physikalisch motivierten Modellen vorgestellt. Beide Ansätze basieren auf realen Sensormessdaten und zielen auf eine leichte Übertragbarkeit sowie die Möglichkeit der Erweiterung der Modelle für verschiedene Anwendungsbereiche

3D Shape Cropping

International audienceWe introduce shape cropping as the segmentation of a bounding geometry of an object as observed by sensors with different modalities. Segmenting a bounding volume is a preliminary step in many multi-view vision applications that consider or require the recovery of 3D information, in particular in multi-camera environments. Recent vision systems used to acquire such information often combine sensors of different types, usually color and depth sensors. Given depth and color images we present an efficient geometric algorithm to compute a polyhedral bounding sur- face that delimits the region in space where the object lies. The resulting cropped geometry eliminates unwanted space regions and enables the initialization of further processes including surface refinements. Our approach ex- ploits the fact that such a region can be defined as the intersection of 3D regions identified as non empty in color or depth images. To this purpose, we propose a novel polyhedron combination algorithm that overcomes compu- tational and robustness issues exhibited by traditional intersection tools in our context. We show the correction and effectiveness of the approach on various combination of inputs

Ray Tracing acceleration through a custom scheduling policy to take advantage of the cache affinity in a Linux-based Special-Purpose Operating System

Proyecto de Graduación (Maestría en Electrónica) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería Electrónica, 2021Esta investigación explora el beneficio de diseñar una política de calendarizacion personalizada que reduzca el tiempo de de ejecución de cargas computacionalmente intensivas. Cargas computacionalmente intensivas tales como ray tracing, son sensibles al cambio de contexto producido por el calendarizador. La política de calendarización propuesta asigna afinidad de cache fuerte para reducir el cambio de contexto al permitir que cada hilo tenga asignado un único núcleo para su ejecución. Utilizando un sistema operativo de propósito especifico, hipotéticamente, el sistema tendrá un mayor rendimiento al combinarlo con la política de calendarización personalizada. El algoritmo de ray tracing fue seleccionado como carga computacionalmente intensiva para comparar su rendimiento en un sistema operativo de propósito especifico contra un sistema operativo de propósito general con su configuración por defecto. Comparado a la referencia, ANOVA factorial confirmo un 19% de reducción en el tiempo de sintetizado promedio al usar la política de calendarización personalizada en un sistema operativo de propósito especifico.The present research explores the benefit of designing a custom scheduling policy to reduce the execution time for computationally intensive workloads. Computationally intensive workloads, such as, ray tracing, are sensible to the context switching produced by the scheduler. The proposed custom scheduling policy assigns hard cache affinity to reduce the context switching by allowing each thread to use only one core during the process execution. Utilizing a special-purpose operating system will hypothetically boost the reduced execution time by integrating the custom scheduling policy. Ray tracing algorithm was selected as the computationally intensive workload to compare its performance in the special-purpose operating system with the custom scheduling policy against a generalpurpose operating system with the default configuration. Compared to the baseline, the factorial ANOVA test confirmed an average 19% reduction of the rendering time using the custom scheduling policy in a special-purpose operating system

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author

QuickCSG: Fast Arbitrary Boolean Combinations of N Solids

QuickCSG computes the result for general N-polyhedron boolean expressions without an intermediate tree of solids. We propose a vertex-centric view of the problem, which simplifies the identification of final geometric contributions, and facilitates its spatial decomposition. The problem is then cast in a single KD-tree exploration, geared toward the result by early pruning of any region of space not contributing to the final surface. We assume strong regularity properties on the input meshes and that they are in general position. This simplifying assumption, in combination with our vertex-centric approach, improves the speed of the approach. Complemented with a task-stealing parallelization, the algorithm achieves breakthrough performance, one to two orders of magnitude speedups with respect to state-of-the-art CPU algorithms, on boolean operations over two to dozens of polyhedra. The algorithm also outperforms GPU implementations with approximate discretizations, while producing an output without redundant facets. Despite the restrictive assumptions on the input, we show the usefulness of QuickCSG for applications with large CSG problems and strong temporal constraints, e.g. modeling for 3D printers, reconstruction from visual hulls and collision detection

Sensormodelle zur Simulation der Umfelderfassung für Systeme des automatisierten Fahrens

The use of sensor models allows the simulation of environmental perception in automated driving systems, aiding in development and testing efforts. This work systematically discusses the different types of sensor models and introduces an architecture for statistics based as well as for physically motivated sensor models. Each approach is grounded in real world observations of sensor measurements and is designed for portability and the ease of further extensions.Die Nutzung von Sensormodellen für die Umfelderfassung ebnet den Weg für die simulationsgestützte Entwicklung von Systemen des automatisierten Fahrens. In dieser Arbeit wird eine Systematik für verschiedene Arten von Sensormodellen eingeführt und eine Umsetzung von statistischen sowie von physikalisch motivierten Modellen vorgestellt. Beide Ansätze basieren auf realen Sensormessdaten und zielen auf eine leichte Übertragbarkeit sowie die Möglichkeit der Erweiterung der Modelle für verschiedene Anwendungsbereiche