Journal ArticleDOI
A survey of structure from motion
TLDR
This survey includes a review of the fundamentals of feature extraction and matching, 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 S fM software.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-called reprojection 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 camera location estimation methods in the literature, followed by discussion of notable techniques for 3D structure recovery. We also cover the basics of the simultaneous 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.read more
Citations
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Proceedings ArticleDOI
Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images
TL;DR: Pix2Vox as mentioned in this paper proposes a context-aware fusion module to adaptively select high-quality reconstructions for each part from different coarse 3D volumes to obtain a fused 3D volume.
Journal ArticleDOI
Evaluating the Performance of Structure from Motion Pipelines
TL;DR: A comparison of different state-of-the-art SfM pipelines in terms of their ability to reconstruct different scenes is reported and an evaluation procedure is proposed that considers both the reconstruction errors as well as the estimation errors of the camera poses used in the reconstruction.
Book ChapterDOI
OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas
TL;DR: In this article, the authors circumvent the challenges associated with acquiring high quality 3D datasets with ground truth depth annotations, by reusing recently released large scale 3D dataset and re-purposing them to omnidirectional images via rendering.
Journal ArticleDOI
Automated continuous construction progress monitoring using multiple workplace real time 3D scans
TL;DR: A new method is proposed, where changes are constantly perceived and as-built model continuously updated during the construction process, instead of periodical scanning of the whole building under construction, which enables more efficient project management.
Posted Content
GRF: Learning a General Radiance Field for 3D Scene Representation and Rendering
Alex Trevithick,Bo Yang +1 more
TL;DR: The key to the approach is to explicitly integrate the principle of multi- view geometry to obtain the internal representations from observed 2D views, guaranteeing the learned implicit representations meaningful and multi-view consistent.
References
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Distinctive Image Features from Scale-Invariant Keypoints
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Book
Multiple view geometry in computer vision
Richard Hartley,Andrew Zisserman +1 more
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Book ChapterDOI
SURF: speeded up robust features
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Journal ArticleDOI
A performance evaluation of local descriptors
TL;DR: It is observed that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best and Moments and steerable filters show the best performance among the low dimensional descriptors.
Book ChapterDOI
Bundle Adjustment - A Modern Synthesis
TL;DR: A survey of the theory and methods of photogrammetric bundle adjustment can be found in this article, with a focus on general robust cost functions rather than restricting attention to traditional nonlinear least squares.