Lap-Fai Yu

TL;DR: A system that automatically synthesizes indoor scenes realistically populated by a variety of furniture objects is presented and whether there is a significant difference in the perceived functionality of the automatically synthesized results relative to furniture arrangements produced by human designers is investigated.

TL;DR: This paper introduces SceneNN, an RGB-D scene dataset consisting of 100 scenes that is used as a benchmark to evaluate the state-of-the-art methods on relevant research problems such as intrinsic decomposition and shape completion.

TL;DR: Evaluation results show that the virtual reality training approach designed to teach individuals how to survive earthquakes, in common indoor environments, is effective, with participants who are trained by the approach performing better, on average, than those trained by alternative approaches in terms of the capabilities to avoid physical damage and to detect potentially dangerous objects.

TL;DR: A shading-based shape refinement algorithm which uses a noisy, incomplete depth map from Kinect to help resolve ambiguities in shape-from-shading and refinement of surface normals using a noisy depth map leads to high-quality 3D surfaces.

TL;DR: A novel data-driven approach where nonlinear regressors are trained to capture the relationship between the agent-based metrics, and the geometrical and topological features of a layout is proposed, which can synthesize crowd-aware layouts and improve existing layouts with better crowd flow properties.