Enrique Rosales

Enrique Rosales, Jafet Rodriguez, Chrystiano Araújo et al.

Spatial drawing using ruled-surface brush strokes is a popular mode of content creation in immersive VR, yet little is known about the usability of existing spatial drawing interfaces or potential improvements. We address these questions in a three-phase study. (1) Our exploratory need-finding study (N=8) indicates that popular spatial brushes require users to perform large wrist motions, causing physical strain. We speculate that this is partly due to constraining users to align their 3D controllers with their intended stroke normal orientation. (2) We designed and implemented a new brush interface that significantly reduces the physical effort and wrist motion involved in VR drawing, with the additional benefit of increasing drawing accuracy. We achieve this by relaxing the normal alignment constraints, allowing users to control stroke rulings, and estimating normals from them instead. (3) Our comparative evaluation of StripBrush (N=17) against the traditional brush shows that StripBrush requires significantly less physical effort and allows users to more accurately depict their intended shapes while offering competitive ease-of-use and speed.

Yuan Yao, Nico Schertler, Enrique Rosales et al.

Reconstruction of a 3D shape from a single 2D image is a classical computer vision problem, whose difficulty stems from the inherent ambiguity of recovering occluded or only partially observed surfaces. Recent methods address this challenge through the use of largely unstructured neural networks that effectively distill conditional mapping and priors over 3D shape. In this work, we induce structure and geometric constraints by leveraging three core observations: (1) the surface of most everyday objects is often almost entirely exposed from pairs of typical opposite views; (2) everyday objects often exhibit global reflective symmetries which can be accurately predicted from single views; (3) opposite orthographic views of a 3D shape share consistent silhouettes. Following these observations, we first predict orthographic 2.5D visible surface maps (depth, normal and silhouette) from perspective 2D images, and detect global reflective symmetries in this data; second, we predict the back facing depth and normal maps using as input the front maps and, when available, the symmetric reflections of these maps; and finally, we reconstruct a 3D mesh from the union of these maps using a surface reconstruction method best suited for this data. Our experiments demonstrate that our framework outperforms state-of-the art approaches for 3D shape reconstructions from 2D and 2.5D data in terms of input fidelity and details preservation. Specifically, we achieve 12% better performance on average in ShapeNet benchmark dataset, and up to 19% for certain classes of objects (e.g., chairs and vessels).