Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality

Real-time occlusion handling is a major problem in outdoor mixed reality system because it requires great computational cost mainly due to the complexity of the scene. Using only segmentation, it is difficult to accurately render a virtual object occluded by complex objects such as trees, bushes etc. In this paper, we propose a novel occlusion handling method for real-time, outdoor, and omni-directional mixed reality system using only the information from a monocular image sequence. We first present a semantic segmentation scheme for predicting the amount of visibility for different type of objects in the scene. We also simultaneously calculate a foreground probability map using depth estimation derived from optical flow. Finally, we combine the segmentation result and the probability map to render the computer generated object and the real scene using a visibility-based rendering method. Our results show great improvement in handling occlusions compared to existing blending based methods

Shadow Generation in Augmented Reality: A Complete Survey

This paper provides an overview of the issues and techniques involved in shadow generation in mixed reality environments. Shadow generation techniques in virtual environments are explained briefly. The key factors characterizing the well-known techniques are described in detail and the pros and cons of each technique are discussed. The conceptual perspective, the improvements, and future techniques are also investigated, sum- marized, and analysed in depth. This paper aims to provide researchers with a solid background on the state- of-the-art implementation of shadows in mixed reality. Thus, this could make it easier to choose the most appropriate method to achieve the aims. It is also hoped that this analysis will help researchers find solutions to the problems facing each technique

Static scene illumination estimation from video with applications

We present a system that automatically recovers scene geometry and illumination from a video, providing a basis for various applications. Previous image based illumination estimation methods require either user interaction or external information in the form of a database. We adopt structure-from-motion and multi-view stereo for initial scene reconstruction, and then estimate an environment map represented by spherical harmonics (as these perform better than other bases). We also demonstrate several video editing applications that exploit the recovered geometry and illumination, including object insertion (e.g., for augmented reality), shadow detection, and video relighting

Shadow Generation in Mixed Reality: A Comprehensive Survey

This paper provides an overview of the issues and techniques involved in shadow generation in mixed reality environments. Shadow generation techniques in virtual environments are explained briefly. The key factors characterizing the well-known techniques are described in detail and the pros and cons of each technique are discussed. The conceptual perspective, the improvements, and future techniques are also investigated, summarized, and analysed in depth. This paper aims to provide researchers with a solid background on the state-of-the-art implementation of shadows in mixed reality. Thus, this could make it easier to choose the most appropriate method to achieve the aims. It is also hoped that this analysis will help researchers find solutions to the problems facing each technique

A Dataset of Multi-Illumination Images in the Wild

Collections of images under a single, uncontrolled illumination have enabled the rapid advancement of core computer vision tasks like classification, detection, and segmentation. But even with modern learning techniques, many inverse problems involving lighting and material understanding remain too severely ill-posed to be solved with single-illumination datasets. To fill this gap, we introduce a new multi-illumination dataset of more than 1000 real scenes, each captured under 25 lighting conditions. We demonstrate the richness of this dataset by training state-of-the-art models for three challenging applications: single-image illumination estimation, image relighting, and mixed-illuminant white balance.Comment: ICCV 201