Real Time Shadow Mapping for Augmented Reality Photorealistic Rendering

In this paper, we present a solution for the photorealistic ambient light render of holograms into dynamic real scenes, in augmented reality applications. Based on Microsoft HoloLens, we achieved this result with an Image Base Lighting (IBL) approach. The real-time image capturing that has been designed is able to automatically locate and position directional lights providing the right illumination to the holograms. We also implemented a negative “shadow drawing” shader that contributes to the final photorealistic and immersive effect of holograms in real life. The main focus of this research was to achieve a superior photorealism through the combination of real-time lights placement and negative “shadow drawing” shader. The solution was evaluated in various Augmented Reality case studies, from classical ones (using Vuforia Toolkit) to innovative applications (using HoloLens).

GPVC: Graphics Pipeline-Based Visibility Classification for Texture Reconstruction

The shadow-mapping and ray-tracing algorithms are the two popular approaches used in visibility handling for multi-view based texture reconstruction. Visibility testing based on the two algorithms needs a user-defined bias to reduce computation error. However, a constant bias does not work for every part of a geometry. Therefore, the accuracy of the two algorithms is limited. In this paper, we propose a high-precision graphics pipeline-based visibility classification (GPVC) method without introducing a bias. The method consists of two stages. In the first stage, a shader-based rendering is designed in the fixed graphics pipeline to generate initial visibility maps (IVMs). In the second stage, two algorithms, namely, lazy-projection coverage correction (LPCC) and hierarchical iterative vertex-edge-region sampling (HIVERS), are proposed to classify visible primitives into fully visible or partially visible primitives. The proposed method can be easily implemented in the graphics pipeline to achieve parallel acceleration. With respect to efficiency, the proposed method outperforms the bias-based methods. With respect to accuracy, the proposed method can theoretically reach a value of 100%. Compared with available libraries and software, the textured model based on our method is smoother with less distortion and dislocation.