3D Car Shape Reconstruction from a Single Sketch Image

Efficient car shape design is a challenging problem in both the automotive industry and the computer animation/games industry. In this paper, we present a system to reconstruct the 3D car shape from a single 2D sketch image. To learn the correlation between 2D sketches and 3D cars, we propose a Variational Autoencoder deep neural network that takes a 2D sketch and generates a set of multiview depth & mask images, which are more effective representation comparing to 3D mesh, and can be combined to form the 3D car shape. To ensure the volume and diversity of the training data, we propose a feature-preserving car mesh augmentation pipeline for data augmentation. Since deep learning has limited capacity to reconstruct fine-detail features, we propose a lazy learning approach that constructs a small subspace based on a few relevant car samples in the database. Due to the small size of such a subspace, fine details can be represented effectively with a small number of parameters. With a low-cost optimization process, a high-quality car with detailed features is created. Experimental results show that the system performs consistently to create highly realistic cars of substantially different shape and topology, with a very low computational cost

Single Sketch Image based 3D Car Shape Reconstruction with Deep Learning and Lazy Learning

Efficient car shape design is a challenging problem in both the automotive industry and the computer animation/games industry. In this paper, we present a system to reconstruct the 3D car shape from a single 2D sketchimage. To learn the correlation between 2D sketches and 3D cars, we propose a Variational Autoencoder deepneural network that takes a 2D sketch and generates a set of multi-view depth and mask images, which forma more effective representation comparing to 3D meshes, and can be effectively fused to generate a 3D carshape. Since global models like deep learning have limited capacity to reconstruct fine-detail features, wepropose a local lazy learning approach that constructs a small subspace based on a few relevant car samples inthe database. Due to the small size of such a subspace, fine details can be represented effectively with a smallnumber of parameters. With a low-cost optimization process, a high-quality car shape with detailed featuresis created. Experimental results show that the system performs consistently to create highly realistic cars ofsubstantially different shape and topology

27. Chemical Composition of Japanese Granites : Part 1. Variation Trends of 400 Analyses

About 400 superior-quality analyses of Japanese granitic rocks are plotted in THOKNTON and TUTTLE'S variation diagrams and projections in the Q-Or-Ab-An system. In the variation diagrams, concentration of points is fairly good, forming one broad but well-defined petrographic province. As compared with WASHINGTON'S 5000 igneous rocks, Japanese granites are slightly higher in SiO2 and lower in Fe2O3, Na2O, and K2O. Most of the analyses show considerably high amounts of An when plotted in the Q-Or-Ab-An tetrahedron. Normative compositions of Paleozoic Japanese pelitic sediments are very low in An. It is not possible to form most of the Japanese granitic liquids by partial or total fusion of such pelitic sediments.|服部・野沢(1959)のまとめた日本の花崗岩質岩石の分析値のうち,適当なもの約400個をTHORNTON・TUTTLEの変化図やQ-Or-Ab-An系に投影してみた.変化図においては,点は直線上に集中し,分散は比較的小さく,全休として1つの特徴的な岩石区を示す.WASHINGTONの5000個の火成岩の変化図と比較すると,日本の花崗岩類は,SiO2にやや富み,Fe2O3,FeO,Na2O,K2Oにやや乏しい.花崗岩質成分をよく近似するQ-Or-Ab-Anの4面体に投影すると,点は可成分散するが,An成分に著しく富むものが多い.また点はOr側よりもAn側に偏つて集中する.日本の古生層の粘土質堆積岩の組成を同じ4面体に投影すると,An成分に乏しいのが特徴である.従つて,このような粘土質堆積岩の部分溶融または全溶融によつては,日本の花崗岩質マグマは生じない

3D car shape reconstruction from a contour sketch using GAN and lazy learning

3D car models are heavily used in computer games, visual effects, and even automotive designs. As a result, producing such models with minimal labour costs is increasingly more important. To tackle the challenge, we propose a novel system to reconstruct a 3D car using a single sketch image. The system learns from a synthetic database of 3D car models and their corresponding 2D contour sketches and segmentation masks, allowing effective training with minimal data collection cost. The core of the system is a machine learning pipeline that combines the use of a generative adversarial network (GAN) and lazy learning. GAN, being a deep learning method, is capable of modelling complicated data distributions, enabling the effective modelling of a large variety of cars. Its major weakness is that as a global method, modelling the fine details in the local region is challenging. Lazy learning works well to preserve local features by generating a local subspace with relevant data samples. We demonstrate that the combined use of GAN and lazy learning produces is able to produce high-quality results, in which different types of cars with complicated local features can be generated effectively with a single sketch. Our method outperforms existing ones using other machine learning structures such as the variational autoencoder