Abstract
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.
Highlights
Real-world 3D reconstruction is a longstanding goal in computer vision
We focus on high-quality 3D reconstruction of indoor scenes with consumer RGBD cameras, and review the methods in terms of depth image processing, camera pose estimation, and surface reconstruction
Sparse-to-dense [77] first introduced a robust and accurate depth estimation method from RGB images with additional sparse depth samples acquired from a low-resolution depth sensor; it was used in a simultaneous localization and mapping (SLAM) system
Summary
Real-world 3D reconstruction is a longstanding goal in computer vision. Many tools have been applied to accurately perceive the 3D world, including stereo cameras, laser range finders, monocular cameras, and RGB-D cameras. Advances in consumer RGBD cameras, such as the Microsoft Kinect, Asus Xtion Live, Intel RealSense, Google Tango, and Occiptial’s Structure Sensor, facilitate numerous new and exciting applications, e.g., in augmented reality (AR) to fuse supplementary elements with the real-world environment (e.g., Holoportation [1]), in virtual reality (VR) to provide users with reliable environment perception [2], in digital cultural heritage protection for realistic modeling [3], and in simultaneous localization and mapping (SLAM) for automatic robot navigation. We focus on high-quality 3D reconstruction of indoor scenes with consumer RGBD cameras, and review the methods in terms of depth image processing, camera pose estimation, and surface reconstruction.
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