Manhattan Junction Catalogue for Spatial Reasoning of Indoor Scenes

Ramalingam, Srikumar; Pillai, Jaishanker K.; Jain, Arpit; Taguchi, Yuichi

doi:10.1109/cvpr.2013.394

Cited by 71 publications

(56 citation statements)

References 25 publications

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“…This sparked a renaissance during which progress started being made on a variety of long-standing 3D understanding problems. In the indoor world, a great deal of effort went into developing constrained models for the prediction of room layout [10] as well as features [6,23,27] and effective methods for inference [4,22,31,32]. While these high-level constraints have been enormously successful in constrained domains (e.g., less cluttered scenes with visible floors such as the datasets of [10,38]), they have not been successfully demonstrated on highly cluttered scenes such as the NYU v2 Depth Dataset [33].…”

Section: Related Workmentioning

confidence: 99%

Unfolding an Indoor Origami World

Fouhey

Gupta

Hebert

2014

Lecture Notes in Computer Science

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Abstract. In this work, we present a method for single-view reasoning about 3D surfaces and their relationships. We propose the use of midlevel constraints for 3D scene understanding in the form of convex and concave edges and introduce a generic framework capable of incorporating these and other constraints. Our method takes a variety of cues and uses them to infer a consistent interpretation of the scene. We demonstrate improvements over the state-of-the art and produce interpretations of the scene that link large planar surfaces.

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Section: Related Workmentioning

confidence: 99%

Unfolding an Indoor Origami World

Fouhey

Gupta

Hebert

2014

Lecture Notes in Computer Science

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“…Under the Manhattan world assumption, an efficient voting scheme was introduced recently for computing the junc- tion features [22]. In our work, we use these junction features for designing the penalty terms in the LP.…”

Section: Junction-breaking Costsmentioning

confidence: 99%

“…Jain et al [15] used connectivity constraints for reconstructing lines from multiple images. Recently, it was shown that junction features can be extracted from real images using an efficient voting scheme [22]. We use junctions for designing penalty terms in a linear programming (LP) formulation.…”

Section: Related Workmentioning

confidence: 99%

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Lifting 3D Manhattan Lines from a Single Image

Ramalingam

Brand

2013

2013 IEEE International Conference on Computer Vision

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We propose a novel and an efficient method for reconstructing the 3D arrangement of lines extracted from a single image, using vanishing points, orthogonal structure, and an optimization procedure that considers all plausible connectivity constraints between lines. Line detection identifies a large number of salient lines that intersect or nearly intersect in an image, but relatively a few of these apparent junctions correspond to real intersections in the 3D scene. We use linear programming (LP) to identify a minimal set of least-violated connectivity constraints that are sufficient to unambiguously reconstruct the 3D lines. In contrast to prior solutions that primarily focused on well-behaved synthetic line drawings with severely restricting assumptions, we develop an algorithm that can work on real images. The algorithm produces line reconstruction by identifying 95% correct connectivity constraints in York Urban database, with a total computation time of 1 second per image. IEEE International Conference on Computer Vision (ICCV)This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.

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“…Flint et al [27] addressed the spatial layout estimation problem by integrating information from image features, stereo features, and 3-D point clouds in a MAP optimization problem, which is solved using dynamic programming. Ramalingam et al [28] presented a method to detect junctions formed by line segments in three Manhattan orthogonal directions using a voting scheme. Possible cuboid layouts generated from the junctions are evaluated using an inference algorithm based on a conditional random field model.…”

Section: Related Workmentioning

confidence: 99%

Indoor Manhattan spatial layout recovery from monocular videos via line matching

Kim

Manduchi

2017

Computer Vision and Image Understanding

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We present an end-to-end system for structure and motion computation in a Manhattan layout from monocular videos. Unlike most SFM algorithms that rely on point feature matching, only line matches are considered in this work. This may be convenient in indoor environment characterized by extended textureless walls, where point features may be scarce. Our system relies on the notion of "characteristic lines", which are invariants of two views of the same parallel line pairs on a surface of known orientation. Experiments with indoor video sequences demonstrate the robustness of the proposed system.

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Manhattan Junction Catalogue for Spatial Reasoning of Indoor Scenes

Abstract: Abstract

Cited by 71 publications

References 25 publications

Unfolding an Indoor Origami World

Unfolding an Indoor Origami World

Lifting 3D Manhattan Lines from a Single Image

Indoor Manhattan spatial layout recovery from monocular videos via line matching

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