A survey of structure from motion.

Abstract

The structure from motion (SfM) problem in computer vision is to recover the three-dimensional (3D) structure of a stationary scene from a set of projective measurements, represented as a collection of two-dimensional (2D) images, via estimation of motion of the cameras corresponding to these images. In essence, SfM involves the three main stages of (i) extracting features in images (e.g. points of interest, lines,etc.) and matching these features between images, (ii) camera motion estimation (e.g. using relative pairwise camera positions estimated from the extracted features), and (iii) recovery of the 3D structure using the estimated motion and features (e.g. by minimizing the so-calledreprojection error). This survey mainly focuses on relatively recent developments in the literature pertaining to stages (ii) and (iii). More specifically, after touching upon the early factorization-based techniques for motion and structure estimation, we provide a detailed account of some of the recent cameralocationestimation methods in the literature, followed by discussion of notable techniques for 3D structure recovery. We also cover the basics of thesimultaneous localization and mapping(SLAM) problem, which can be viewed as a specific case of the SfM problem. Further, our survey includes a review of the fundamentals of feature extraction and matching (i.e. stage (i) above), various recent methods for handling ambiguities in 3D scenes, SfM techniques involving relatively uncommon camera models and image features, and popular sources of data and SfM software.

Ground-Plane-Based Absolute Scale Estimation for Monocular Visual Odometry

“…A comprehensive review of these works is beyond the scope of this paper. Some detailed summaries of them can be found in [11], [12], [13] and [14]. In this section, we only discuss latest works related to our absolute scale estimation problem.…”

Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction

“…3D shapes can be recovered from multiple color images or depth scans. To estimate the underlying 3D shape from multiple color images, classic SfM [20] and vSLAM [3] algorithms firstly extract and match hand-crafted geometric features [10] and then apply bundle adjustment [26] for both shape and camera motion estimation. Ji et al [14] use "maximizing rigidity" for reconstruction, but this requires 2D point correspondences across images.…”

Robust Motion Segmentation From Pairwise Matches

“…The goal of multibody structure from motion is to simultaneously estimate the motion between each object and the camera as well as the 3D structure of each object, given a set of images of a dynamic scene. This problem can be seen as the generalization of structure from motion [32] to the dynamic case, where motion segmentation has to be solved in addition to 3D reconstruction. Geometric solutions are available for two images [51] and three images [49].…”