Mobile graphics: SIGGRAPH Asia 2017 course

Peer ReviewedPostprint (published version

Context-aware mass customization construction system: methods for user captured as-built plans

The problem of context, a fundamental aspect of dealing with built environments, has not been adequately addressed by mass customization systems so far, which has limited their scope of application. The aim of the present article is to evaluate the adequacy of existing methods of producing as-built plans of rooms by non-expert users for the automatic generation and production of partition walls for building renovation. This paper highlights criteria to develop appropriate methods of capturing context for mass customization construction systems.info:eu-repo/semantics/publishedVersio

Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction

Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view images is a fundamental yet active research area in computer vision. Despite the steady progress in multi-view stereo reconstruction, most existing methods are still limited in recovering fine-scale details and sharp features while suppressing noises, and may fail in reconstructing regions with few textures. To address these limitations, this paper presents a Detail-preserving and Content-aware Variational (DCV) multi-view stereo method, which reconstructs the 3D surface by alternating between reprojection error minimization and mesh denoising. In reprojection error minimization, we propose a novel inter-image similarity measure, which is effective to preserve fine-scale details of the reconstructed surface and builds a connection between guided image filtering and image registration. In mesh denoising, we propose a content-aware $\ell_{p}$-minimization algorithm by adaptively estimating the $p$ value and regularization parameters based on the current input. It is much more promising in suppressing noise while preserving sharp features than conventional isotropic mesh smoothing. Experimental results on benchmark datasets demonstrate that our DCV method is capable of recovering more surface details, and obtains cleaner and more accurate reconstructions than state-of-the-art methods. In particular, our method achieves the best results among all published methods on the Middlebury dino ring and dino sparse ring datasets in terms of both completeness and accuracy.Comment: 14 pages,16 figures. Submitted to IEEE Transaction on image processin

The toulouse vanishing points dataset

International audienceIn this paper we present the Toulouse Vanishing Points Dataset, a public photographs database of Manhattan scenes taken with an iPad Air 1. The purpose of this dataset is the evaluation of vanishing points estimation algorithms. Its originality is the addition of Inertial Measurement Unit (IMU) data synchronized with the camera under the form of rotation matrices. Moreover, contrary to existing works which provide vanishing points of reference in the form of single points, we computed uncertainty regions. The Toulouse Vanishing Points Dataset is publicly available at http://ubee.enseeiht.fr/tvp

Mobile Framework for CT Image Reconstruction

Mobile devices have conquered the world from a common daily usage as e-mail to a complex application as Global Positioning System. The mobile devices have a potential to be developed as a computed device with an application to reconstruct images from computed tomography. The mobile CT application was developed to visualize the CT datasets by plotting out a test dataset to form a sinogram image on the mobile device’s screen. The image was obtained by reconstructed the CT datasets using filtered backprojection image processing algorithm. The CT datasets were filtered by using filtered datasets before the image reconstruction processes. The filtering process was a method to remove the blurring effect of the backprojection algorithm