Point cloud completion via structured feature maps using a feedback network

In this paper,we tackle the challenging problem of point cloud completion from the perspective of feature learning.Our key observation is that to recover the underlying structures as well as surface details,given partial input,a fundamental component is a good feature representation that can capture both global structure and local geometric details.We accordingly first propose FSNet,a feature structuring module that can adaptively aggregate point-wise features into a 2D structured feature map by learning multiple latent patterns from local regions.We then integrate FSNet into a coarse-to-fine pipeline for point cloud completion.Specifically,a 2D convolutional neural network is adopted to decode feature maps from FSNet into a coarse and complete point cloud.Next,a point cloud upsampling network is used to generate a dense point cloud from the partial input and the coarse intermediate output.To efficiently exploit local structures and enhance point distribution uniformity,we propose IFNet,a point upsampling module with a self-correction mechanism that can progressively refine details of the generated dense point cloud.We have conducted qualitative and quantitative experiments on ShapeNet,MVP,and KITTI datasets,which demonstrate that our method outperforms stateof-the-art point cloud completion approaches.

Deep convolutional surrogates and freedom in thermal design

A deep learning approach is presented for heat transfer and pressure drop prediction of complex fin geometries generated using composite Bézier curves.Thermal design process includes iterative high fidelity simulation which is complex,computationally expensive,and time-consuming.With the advancement in machine learning algorithms as well as Graphics Processing Units(GPUs),parallel processing architecture of GPUs can be used to accelerate thermo-fluid simulation.In this study,Convolutional Neural Networks(CNNs)are used to predict results of Computational Fluid Dynamics(CFD)directly from topologies saved as images.A design space with a single fin as well as multiple morphable fins are studied.A comparison of Xception network and regular CNN is presented for the case with a single fin design.Results show that high accuracy in prediction is observed for single fin design particularly using Xception network.Xception network provides 98 percent accuracy in heat transfer and pressure drop prediction of the single fin design.Increasing the design freedom to multiple fins increases the error in prediction.This error,however,remains within three percent of the ground truth values which is valuable for design purpose.The presented predictive model can be used for innovative BREP-based fin design optimization in compact and high efficiency heat exchangers.

Texture Pattern Generation Using Clonal Mosaic

In this paper, an effective system for synthesizing animal skin patterns on arbitrary polygonal surfaces is developed. To accomplish the task, a system inspired by the Clonal Mosaic （CM） model is proposed. The CM model simulates cells＇ reactions on arbitrary surface. By controlling the division, mutation and repulsion of cells, a regulated spatial arrangement of cells is formed. This arrangement of cells shows appealing result, which is comparable with those natural patterns observed from animal skin. However, a typical CM simulation process incurs high computational cost, where the distances among ceils across a polygonal surface are measured and the movements of cells are constrained on the surface. In this framework, an approach is proposed to transform each of the original 3D geometrical planes of the surface into its Canonical Reference Plane Structure. This structure helps to simplify a 3D computational problem into a more manageable 2D problem. Furthermore, the concept of Local Relaxation is developed to optimally enhance the relaxation process for a typical CM simulation. The performances of the proposed solution methods have been verified with extensive experimental results.

Point-Based Data Analysis for Extracting Parameters of Cutting Tools

Various types of cutting tools are known and are in use for machining parts. The dimensional parameters associated with cutting tools need to be estimated and compared to the desired values for determining their cutting performance. In this paper, a data analysis methodology for extracting parameters from a measured point set corresponding to the surface of a cutting tool is provided. We propose that the 3-D data can be simplified into 2-D data or regular data by virtually slicing it at a predetermined section or by projecting it onto a same axial plane after a simple fixed-axis rotation. A plurality of curves can be generated and optimized based on the obtained 2-D points on a cross section for calculating the section parameters, including radial (axial) rake angle, relief angle, and land width. Other dimensional parameters can also be extracted from the contour of the presented rotary axial projection data. The experimental results have shown that the approaches elaborated in this paper are effective and robust, which can be potentially extended to other applications such as the inspection of similar parts and their parameters extraction.