Methodology for automatic recovering of 3D partitions from unstitched faces of non-manifold CAD models

Data exchanges between different software are currently used in industry to speed up the preparation of digital prototypes for Finite Element Analysis (FEA). Unfortunately, due to data loss, the yield of the transfer of manifold models rarely reaches 1. In the case of non-manifold models, the transfer results are even less satisfactory. This is particularly true for partitioned 3D models: during the data transfer based on the well-known exchange formats, all 3D partitions are generally lost. Partitions are mainly used for preparing mesh models required for advanced FEA: mapped meshing, material separation, definition of specific boundary conditions, etc. This paper sets up a methodology to automatically recover 3D partitions from exported non-manifold CAD models in order to increase the yield of the data exchange. Our fully automatic approach is based on three steps. First, starting from a set of potentially disconnected faces, the CAD model is stitched. Then, the shells used to create the 3D partitions are recovered using an iterative propagation strategy which starts from the so-called manifold vertices. Finally, using the identified closed shells, the 3D partitions can be reconstructed. The proposed methodology has been validated on academic as well as industrial examples.This work has been carried out under a research contract between the Research and Development Direction of the EDF Group and the Arts et Métiers ParisTech Aix-en-Provence

A constraint manager to support virtual maintainability

Virtual prototyping tools have already captivated the industry's interest as viable design tool. One of the key challenges for the research community is to extend the capabilities of Virtual Reality technology beyond its current scope of ergonomics and design reviews. The research presented in this paper is part of a larger research programme that aims to perform maintainability assessment on virtual prototypes. This paper discusses the design and implementation of a geometric constraint manager that has been designed to support physical realism and interactive assembly and disassembly tasks within virtual environments. The key techniques employed by the constraint manager are direct interaction, automatic constraint recognition, constraint satisfaction and constrained motion. Various optimization techniques have been implemented to achieve real-time interaction with large industrial models

Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer

Semantic annotations are vital for training models for object recognition, semantic segmentation or scene understanding. Unfortunately, pixelwise annotation of images at very large scale is labor-intensive and only little labeled data is available, particularly at instance level and for street scenes. In this paper, we propose to tackle this problem by lifting the semantic instance labeling task from 2D into 3D. Given reconstructions from stereo or laser data, we annotate static 3D scene elements with rough bounding primitives and develop a model which transfers this information into the image domain. We leverage our method to obtain 2D labels for a novel suburban video dataset which we have collected, resulting in 400k semantic and instance image annotations. A comparison of our method to state-of-the-art label transfer baselines reveals that 3D information enables more efficient annotation while at the same time resulting in improved accuracy and time-coherent labels.Comment: 10 pages in Conference on Computer Vision and Pattern Recognition (CVPR), 201

Morphological evolution of a 3D CME cloud reconstructed from three viewpoints

The propagation properties of coronal mass ejections (CMEs) are crucial to predict its geomagnetic effect. A newly developed three dimensional (3D) mask fitting reconstruction method using coronagraph images from three viewpoints has been described and applied to the CME ejected on August 7, 2010. The CME's 3D localisation, real shape and morphological evolution are presented. Due to its interaction with the ambient solar wind, the morphology of this CME changed significantly in the early phase of evolution. Two hours after its initiation, it was expanding almost self-similarly. CME's 3D localisation is quite helpful to link remote sensing observations to in situ measurements. The investigated CME was propagating to Venus with its flank just touching STEREO B. Its corresponding ICME in the interplanetary space shows a possible signature of a magnetic cloud with a preceding shock in VEX observations, while from STEREO B only a shock is observed. We have calculated three principle axes for the reconstructed 3D CME cloud. The orientation of the major axis is in general consistent with the orientation of a filament (polarity inversion line) observed by SDO/AIA and SDO/HMI. The flux rope axis derived by the MVA analysis from VEX indicates a radial-directed axis orientation. It might be that locally only the leg of the flux rope passed through VEX. The height and speed profiles from the Sun to Venus are obtained. We find that the CME speed possibly had been adjusted to the speed of the ambient solar wind flow after leaving COR2 field of view and before arriving Venus. A southward deflection of the CME from the source region is found from the trajectory of the CME geometric center. We attribute it to the influence of the coronal hole where the fast solar wind emanated from.Comment: ApJ, accepte

Blending Learning and Inference in Structured Prediction

In this paper we derive an efficient algorithm to learn the parameters of structured predictors in general graphical models. This algorithm blends the learning and inference tasks, which results in a significant speedup over traditional approaches, such as conditional random fields and structured support vector machines. For this purpose we utilize the structures of the predictors to describe a low dimensional structured prediction task which encourages local consistencies within the different structures while learning the parameters of the model. Convexity of the learning task provides the means to enforce the consistencies between the different parts. The inference-learning blending algorithm that we propose is guaranteed to converge to the optimum of the low dimensional primal and dual programs. Unlike many of the existing approaches, the inference-learning blending allows us to learn efficiently high-order graphical models, over regions of any size, and very large number of parameters. We demonstrate the effectiveness of our approach, while presenting state-of-the-art results in stereo estimation, semantic segmentation, shape reconstruction, and indoor scene understanding