Linear stratified approach using full geometric constraints for 3D scene reconstruction and camera calibration

Abstract

This paper presents a new linear framework to obtain 3D scene reconstruction and camera calibration simultaneously from uncalibrated images using scene geometry. Our strategy uses the constraints of parallelism, coplanarity, colinearity, and orthogonality. These constraints can be obtained in general man-made scenes frequently. This approach can give more stable results with fewer images and allow us to gain the results with only linear operations. In this paper, it is shown that all the geometric constraints used in the previous works performed independently up to now can be implemented easily in the proposed linear method. The study on the situations that cannot be dealt with by the previous approaches is also presented and it is shown that the proposed method being able to handle the cases is more flexible in use. The proposed method uses a stratified approach, in which affine reconstruction is performed first and then metric reconstruction. In this procedure, the additional constraints newly extracted in this paper have an important role for affine reconstruction in practical situations.

A simple method to achieve full-field and real-scale reconstruction using a movable stereo rig

“…Thus the 3D structure is usually recovered with a scale ambiguity problem, which can never be solved from pure image information alone, regardless of how many cameras and points are used. Extra knowledge about points' positions or distances or extra restrictions is always required to fix this problem [15][16][17]. In the case of multi-camera networks calibrated with SFM, point correspondences generally have to be extracted from images in a medium or wide baseline situation, which typically leads to a very high portion of outliers [18,19].…”

Propagation-based incremental triangulation for multiple views 3D reconstruction

“…There has been an escalation in the amount of work dealing with three-dimensional (3D) reconstruction [1][2][3] , pose estimation [4][5][6] , structure-from-motion (SfM) [7,8] , and simultaneous localization and mapping (SLAM) [9,10] , such as applications in robotics, augmented/virtual reality, and self-driving. A fundamental component of these works is triangulation, which refers to recovering the 3D location from multiple two-dimensional (2D) image observations with known pose and camera parameters.…”

Untitled

“…There has been an escalation in the amount of work dealing with three-dimensional (3D) reconstruction [1][2][3] , pose estimation [4][5][6] , structure-from-motion (SfM) [7,8] , and simultaneous localization and mapping (SLAM) [9,10] , such as applications in robotics, augmented/virtual reality, and self-driving. A fundamental component of these works is triangulation, which refers to recovering the 3D location from multiple two-dimensional (2D) image observations with known pose and camera parameters.…”