A Survey on Structure from Motion
By Onur Özyesil, Vladislav Voroninski, Ronen Basri, Amit Singer. arXiv.org, 2017
485 citations recorded by the source corpus.
The structure from motion (SfM) problem in computer vision is the problem of recovering the $3$D structure of a stationary scene from a set of projective measurements, represented as a collection of $2$D images, via estimation of motion of the cameras corresponding to these images. In essence, SfM involves the three main stages of (1) extraction of features in images (e.g., points of interest, lines, etc.) and matching of these features between images, (2) camera motion estimation (e.g., using relative pairwise camera poses estimated from the extracted features), (3) recovery of the $3$D structure using the estimated motion and features (e.g., by minimizing the so-called reprojection error). This survey mainly focuses on the relatively recent developments in the literature pertaining to stages (2) and (3). 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 camera location estimation methods in the literature, which precedes the discussion of notable techniques for $3$D structure recovery. We also cover the basics of the simultaneous localization and mapping (SLAM) problem, which can be considered to be a specific case of the SfM problem. Additionally, a review of the fundamentals of feature extraction and matching (i.e., stage (1) above), various recent methods for handling ambiguities in $3$D scenes, SfM techniques involving relatively uncommon camera models and image features, and popular sources of data and SfM software is included in our survey.
View Paper: https://www.semanticscholar.org/paper/6562880ada35f0410ad6c4db941586bb2b0ebdb1
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Citations in the corpus
- Emergent Correspondence from Image Diffusion (2023, background): “Establishing visual correspondences between different images is crucial for various computer vision tasks such as Structure-from-Motion / 3D reconstruction [2, 74, 61, 75], object tracking [23, 93], image recognition [64, 81, 9] and segmentation [51, 48, 73, 30].”
- Grounding Image Matching in 3D with MASt3R (2024, background): “…correspondences between pixels across different images of the same scene, denoted as image matching , constitutes a core component of all 3D vision applications, spanning mapping [14,61], localization [41,72], navigation [15], photogrammetry [34, 64] and autonomous robotics in general [63,87].”
- DUSt3R: Geometric 3D Vision Made Easy (2023, unknown): “Not only does it have numerous applications like mapping [13, 73] , navigation [15], archaeology [87, 133], cultural heritage preservation [38], robotics [79], but perhaps more importantly, it holds a fundamentally special place among all 3D vision tasks.”
- Pix2Vox: Context-Aware 3D Reconstruction From Single and Multi-View Images (2019, methodology): “of freedom in the point cloud representation because of the limited connections between points. Consequently, these methods cannot recover 3D volumes accurately [29]. Multi-view 3D Reconstruction SfM [14] and SLAM [6] methods are successful in handling many scenarios. These methods match features am”
- Multi-view 3D Reconstruction with Transformers (2021, methodology): “Traditional methods [30, 9] of this field are typically designed based on hand-crafted geometric features.”
- VGGT: Visual Geometry Grounded Transformer (2025, unknown): “Structure from Motion is a classic computer vision problem [33, 52, 54] that involves estimating camera parameters and reconstructing sparse point clouds from a set of images of a static scene captured from different viewpoints.”