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 scene reconstruction. 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.

Virtual reality, augmented reality, robotics, and autonomous driving, have recently attracted much attention from both academic and industrial communities, in which image-based camera localization is a key task. However, there has not been a complete review on image-based camera localization. It is urgent to map this topic to enable individuals enter the field quickly. In this paper, an overview of image-based camera localization is presented. A new and complete classification of image-based camera localization approaches is provided and the related techniques are introduced. Trends for future development are also discussed. This will be useful not only to researchers, but also to engineers and other individuals interested in this field.

Simultaneous localization and mapping (SLAM) has been studied for decades in the field of robotics, in which light detection and ranging (LiDAR) is widely used in various application areas benefiting from its accessibility of direct, accurate, and reliable 3-D measurements. However, the performance of LiDAR SLAM may be degraded when running in degenerate scenario, which makes it still a challenging problem to realize real-time, robust, and accurate state estimation in complex environments. In this article, we propose a keyframe-based 3-D LiDAR SLAM using an accurate principal component analysis (PCA)-based feature extraction method and an efficient two-stage matching strategy, toward a more robust, accurate, and globally consistent estimation performance. The effectiveness and performance are demonstrated and evaluated by comparing our method with the state-of-the-art open-source methods, LOAM and LeGo-LOAM, on KITTI datasets and custom datasets collected by our sensor system. The experimental results show obvious improvement of odometry accuracy and mapping consistency without loss of real-time performance.