PG2018 Short Papers and Posters

Permanent URI for this collection

Now showing 1 - 1 of 1

Bottom-up/Top-down Geometric Object Reconstruction with CNN Classification for Mobile Education
(The Eurographics Association, 2018) Guo, Ting; Cui, Rundong; Qin, Xiaoran; Wang, Yongtao; Tang, Zhi; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Geometric objects in educational materials are often illustrated as 2D line drawings, which results in the loss of depth information. To alleviate the problem of fully understanding the 3D structure of geometric objects, we propose a novel method to reconstruct the 3D shape of a geometric object illustrated in a line drawing image. In contrast to most existing methods, ours directly take a single line drawing image as input and generate a valid sketch for reconstruction. Given a single input line drawing image, we first classify the geometric object in the image with convolution neural network (CNN). More specifically, we pre-train the model with simulated images to alleviate the problems of data collection and unbalanced distribution among different classes. Then, we generate the sketch of the geometric object with our proposed bottom-up and top-down scheme. Finally, we finish reconstruction by minimizing an objective function of reconstruction error. Extensive experimental results demonstrate that our method performs significantly better in both accuracy and efficiency compared with the existing methods.