Detecting Vanishing Points Using Global Image Context in a Non-ManhattanWorld

Zhai, Menghua; Workman, Scott; Jacobs, Nathan

doi:10.1109/cvpr.2016.610

Cited by 71 publications

(129 citation statements)

References 34 publications

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“…Earlier work proposes to detect and segment skylines of the images in order to estimate horizon lines [13,2]. More recently, CNN-based techniques have been developed for horizon estimation from a single image [52,19,48]. Most of these methods formulate the problem as either regression or classification and impose a strong prior on the location of features correlated with the visible horizon and of corresponding camera parameters.…”

Section: Related Workmentioning

confidence: 99%

UprightNet: Geometry-Aware Camera Orientation Estimation From Single Images

Xian

Snavely

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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We introduce UprightNet, a learning-based approach for estimating 2DoF camera orientation from a single RGB image of an indoor scene. Unlike recent methods that leverage deep learning to perform black-box regression from image to orientation parameters, we propose an end-to-end framework that incorporates explicit geometric reasoning. In particular, we design a network that predicts two representations of scene geometry, in both the local camera and global reference coordinate systems, and solves for the camera orientation as the rotation that best aligns these two predictions via a differentiable least squares module. This network can be trained end-to-end, and can be supervised with both ground truth camera poses and intermediate representations of surface geometry. We evaluate UprightNet on the single-image camera orientation task on synthetic and real datasets, and show significant improvements over prior state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

UprightNet: Geometry-Aware Camera Orientation Estimation From Single Images

Xian

Snavely

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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“…Since our method does not involve any other refinement steps like expectation maximization etc. as used in [37], it is very fast and takes around 40 milliseconds per image on a lower-middle end GPU (GTX 1050 Ti). This amounts to 25 frames per second, thus making it suitable for application to videos in real time.…”

Section: Datasetsmentioning

confidence: 99%

“…Compared to this, Lezama et al [22] detect line segments in the image initially, and compute vanishing points from them which gives the horizon line. Zhai et al [37] estimates horizon line candidates from the CNN. Then they estimate the zenith vanishing point using these horizon lines.…”

Section: Comparison On the Hlw Datasetmentioning

confidence: 99%

A Geometric Approach to Obtain a Bird's Eye View From an Image

Abbas

Zisserman

2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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The objective of this paper is to rectify any monocular image by computing a homography matrix that transforms it to a bird's eye (overhead) view.We make the following contributions: (i) we show that the homography matrix can be parameterised with only four parameters that specify the horizon line and the vertical vanishing point, or only two if the field of view or focal length is known; (ii) We introduce a novel representation for the geometry of a line or point (which can be at infinity) that is suitable for regression with a convolutional neural network (CNN); (iii) We introduce a large synthetic image dataset with ground truth for the orthogonal vanishing points, that can be used for training a CNN to predict these geometric entities; and finally (iv) We achieve state-of-theart results on horizon detection, with 74.52% AUC on the Horizon Lines in the Wild dataset. Our method is fast and robust, and can be used to remove perspective distortion from videos in real time.

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“…The neural network yields two images, one for the vertical vanishing point and one for the horizontal vanishing point. From every image we take a point with maximum intensity as an answer and transform its coordinates back to the original image coordinates space using equations (8) and (9) mixed with coordinates transform according to convolution layers.…”

Section: Houghnet Nn Architecturementioning

confidence: 99%

HoughNet: Neural Network Architecture for Vanishing Points Detection

Sheshkus¹,

Ingacheva

Arlazarov

et al. 2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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In this paper we introduce a novel neural network architecture based on Fast Hough Transform layer. The layer of this type allows our neural network to accumulate features from linear areas across the entire image instead of local areas. We demonstrate its potential by solving the problem of vanishing points detection in the images of documents. Such problem occurs when dealing with camera shots of the documents in uncontrolled conditions. In this case, the document image can suffer several specific distortions including projective transform. To train our model, we use MIDV-500 dataset and provide testing results. Strong generalization ability of the suggested method is proven with its applying to a completely different ICDAR 2011 dewarping contest. In previously published papers considering this dataset authors measured quality of vanishing point detection by counting correctly recognized words with open OCR engine Tesseract. To compare with them, we reproduce this experiment and show that our method outperforms the state-of-the-art result.

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Detecting Vanishing Points Using Global Image Context in a Non-ManhattanWorld

Cited by 71 publications

References 34 publications

UprightNet: Geometry-Aware Camera Orientation Estimation From Single Images

UprightNet: Geometry-Aware Camera Orientation Estimation From Single Images

A Geometric Approach to Obtain a Bird's Eye View From an Image

HoughNet: Neural Network Architecture for Vanishing Points Detection

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