High-quality 3D reconstruction is an important topic in computer graphics and computer vision with many applications, such as robotics and augmented reality. The advent of consumer RGB-D cameras has made a profound advance in indoor scenereconstruction. For the past few years, researchers have spent significant effort to develop algorithms to capture 3D models with RGB-D cameras. As depth images produced by consumer RGB-D cameras are noisy and incomplete when surfaces are shiny, bright, transparent, or far from the camera, obtaining high- quality 3D scene models is still a challenge for existing systems. We here review high-quality 3D indoor scene reconstruction methods using consumer RGB-D cameras. In this paper, we make comparisons and analyses from the following aspects: (i) depth processing methods in 3D reconstruction are reviewed in terms of enhancement and completion, (ii) ICP-based, feature-based, and hybrid methods of camera pose estimation methods are reviewed, and (iii) surface reconstruction methods are reviewed in terms of surface fusion, optimization, and completion. The performance of state-of-the-art methods is also compared and analyzed. This survey will be useful for researchers who want to follow best practices in designing new high-quality 3D reconstruction methods.

In this paper, a geometry-based point cloud reduction method is proposed, and a real-time mobile augmented reality system is explored for applications in urban environments. We formulate a new objective function which combines the point reconstruction errors and constraints on spatial point distribution. Based on this formulation, a mixed integer programming scheme is utilized to solve the points reduction problem. The mobile augmented reality system explored in this paper is composed of the offline and online stages. At the offline stage, we build up the localization database using structure from motion and compress the point cloud by the proposed point cloud reduction method. While at the online stage, we compute the camera pose in real time by combining an image-based localization algorithm and a continuous pose tracking algorithm. Experimental results on benchmark and real data show that compared with the existing methods, this geometry-based point cloud reduction method selects a point cloud subset which helps the image-based localization method to achieve higher success rate. Also, the experiments conducted on a mobile platform show that the reduced point cloud not only reduces the time consuming for initialization and re-initialization, but also makes the memory footprint small, resulting a scalable and real-time mobile augmented reality system.