Book ChapterDOI
A dynamic programming approach to reconstructing building interiors
Alex Flint,Christopher Mei,David W. Murray,Ian Reid +3 more
- pp 394-407
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TLDR
A dynamic programming solution to the reconstruction problem for "indoor" Manhattan worlds (a sub-class of Manhattan worlds), which deterministically finds the global optimum and exhibits computational complexity linear in both model complexity and image size.Abstract:
A number of recent papers have investigated reconstruction under Manhattan world assumption, in which surfaces in the world are assumed to be aligned with one of three dominant directions [1,2,3,4]. In this paper we present a dynamic programming solution to the reconstruction problem for "indoor" Manhattan worlds (a sub-class of Manhattan worlds). Our algorithm deterministically finds the global optimum and exhibits computational complexity linear in both model complexity and image size. This is an important improvement over previous methods that were either approximate [3] or exponential in model complexity [4]. We present results for a new dataset containing several hundred manually annotated images, which are released in conjunction with this paper.read more
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Proceedings ArticleDOI
Manhattan scene understanding using monocular, stereo, and 3D features
TL;DR: This paper presents a graphical model that relates photometric cues learned from labeled data, stereo photo-consistency across multiple views, and depth cues derived from structure-from-motion point clouds, allowing exact, global inference in ∼100 ms (in addition to feature computation of under one second) without using specialized hardware.
Book ChapterDOI
Discriminatively Trained Dense Surface Normal Estimation
TL;DR: This work proposes a method that combines contextual and segment-based cues and builds a regressor in a boosting framework by transforming the problem into the regression of coefficients of a local coding for dense surface normal estimation from a single image.
Proceedings ArticleDOI
Piecewise Planar and Compact Floorplan Reconstruction from Images
Ricardo Cabral,Yasutaka Furukawa +1 more
TL;DR: This paper presents a system to reconstruct piecewise planar and compact floorplans from images, which are then converted to high quality texture-mapped models for free- viewpoint visualization, and shows that the texture mapped mesh models provide compelling free-viewpoint visualization experiences, when compared against the state-of-the-art and ground truth.
Journal ArticleDOI
Machine Intelligence 6. Edited by B. Meltzer and D. Michie. Pp. 525. £10. 1971. (Edinburgh University Press.)
Proceedings ArticleDOI
Parsing Indoor Scenes Using RGB-D Imagery.
Camillo J. Taylor,Anthony Cowley +1 more
TL;DR: This paper presents an approach to parsing the Manhattan structure of an indoor scene from a single RGBD frame using Dynamic Programming to solve the problem of recovering the floor plan.
References
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Journal ArticleDOI
What energy functions can be minimized via graph cuts
Vladimir Kolmogorov,R. Zabin +1 more
TL;DR: This work gives a precise characterization of what energy functions can be minimized using graph cuts, among the energy functions that can be written as a sum of terms containing three or fewer binary variables.
Journal ArticleDOI
Make3D: Learning 3D Scene Structure from a Single Still Image
TL;DR: This work considers the problem of estimating detailed 3D structure from a single still image of an unstructured environment and uses a Markov random field (MRF) to infer a set of "plane parameters" that capture both the 3D location and 3D orientation of the patch.
Book ChapterDOI
Active Matching
Margarita Chli,Andrew J. Davison +1 more
TL;DR: This paper shows that the dramatically different approach of using priors dynamically to guide a feature by feature matching search can achieve global matching with much fewer image processing operations and lower overall computational cost.
Book
Computer Vision - Eccv 2002
TL;DR: A novel algorithm for recovering a smooth manifold of unknown dimension and topology from a set of points known to belong to it is presented and it can easily be applied when the ambient space is not Euclidean, which is important in many applications.