Discrete-Continuous Depth Estimation from a Single Image

Liu, Miaomiao; Salzmann, Mathieu; He, Xuming

doi:10.1109/cvpr.2014.97

Cited by 365 publications

(220 citation statements)

References 19 publications

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“…The proposed method with the sparse SIFT flow and the reweighted confidence is tested on the NYU data set, and the corresponding results are reported in Table 3. In addition, the computation errors and time by Karsch et al,11 Make3d, 8 discrete-continuous, 27 and Zhuo et al 28 are also reported in Table 3, and a plot of the depth errors is shown in Figure 7(b) at the same time. It is noted that our method is faster than the DepthTransfer, and the speed-up ratio is about 2.10.…”

Section: Evaluations On Nyu Depth Data Setmentioning

confidence: 96%

“…26 Then, this method combined these warped depth maps into an objective function to smooth the resulting depth. More recently, Liu et al 27 explored continuous variables to represent the depth of image superpixels and discrete ones to encode relationships between neighboring superpixels. The depth estimation was formulated as an inference in a highorder, discrete-continuous graphical model, which is realized using particle belief propagation.…”

Section: Related Workmentioning

confidence: 99%

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Fast depth extraction from a single image

Dong

Wang

2016

International Journal of Advanced Robotic Systems

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Predicting depth from a single image is an important problem for understanding the 3-D geometry of a scene. Recently, the nonparametric depth sampling (DepthTransfer) has shown great potential in solving this problem, and its two key components are a Scale Invariant Feature Transform (SIFT) flow-based depth warping between the input image and its retrieved similar images and a pixel-wise depth fusion from all warped depth maps. In addition to the inherent heavy computational load in the SIFT flow computation even under a coarse-to-fine scheme, the fusion reliability is also low due to the low discriminativeness of pixel-wise description nature. This article aims at solving these two problems. First, a novel sparse SIFT flow algorithm is proposed to reduce the complexity from subquadratic to sublinear. Then, a reweighting technique is introduced where the variance of the SIFT flow descriptor is computed at every pixel and used for reweighting the data term in the conditional Markov random fields. Our proposed depth transfer method is tested on the Make3D Range Image Data and NYU Depth Dataset V2. It is shown that, with comparable depth estimation accuracy, our method is 2-3 times faster than the DepthTransfer.

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Section: Evaluations On Nyu Depth Data Setmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

Fast depth extraction from a single image

Dong

Wang

2016

International Journal of Advanced Robotic Systems

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“…Pada [7] menggunakan visual clues sebagai fitur, sedangkan [8] menggunakan superpixel. Selanjutnya, untuk penggunaan CRF dalam estimasi memiliki hasil yang lebih baik dari penggunaan MRF [9] [10]. Fitur superpixel kembali digunakan pada [9], sedangkan [10] lebih memilih menggunakan raw data.…”

Section: Pendahuluanunclassified

“…Selanjutnya, untuk penggunaan CRF dalam estimasi memiliki hasil yang lebih baik dari penggunaan MRF [9] [10]. Fitur superpixel kembali digunakan pada [9], sedangkan [10] lebih memilih menggunakan raw data. Hasil yang diberikan pada [9] dan [10] memiliki perbedaan error relatif yang hampir sama dengan [10] lebih unggul dari [9].…”

Section: Pendahuluanunclassified

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Rachmadi¹,

Alamsyah²

2017

RESTI

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Autonomous UAV is typically require a system which is capable of mapping (segmenting) region of a color image, where the segmentation is a depth image. The depth image can be done by estimate the color image. Estimating color images into depth images is an open challenge in the field of computer vision. The problem of depth image estimation is stochastic based problem. Conditional Random Field (CRF) is a model which is capable of mapping stochastic problems. This study uses CRF as a model of structured classification that is Structured Support Vector Machine (SSVM). In the pre-process, the color image process is converted into superpixel form and using Principal Component Analysis (PCA) as its feature extraction. The results of this study show a good accuracy in the segmentation of the region that is 71.25%.

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“…Superpixels capture redundancy in the image and greatly reduce the complexity of subsequent image processing tasks. Superpixel segmentation has been used successfully in applications, such as image segmentation [25,26], skeletonization [27] or depth estimation [28]. There are many different techniques to generate superpixels, but we will focus on the use of simple linear iterative clustering (SLIC).…”

Section: Superpixel Segmentation and Slicmentioning

confidence: 99%

Superpixel-Based Roughness Measure for Multispectral Satellite Image Segmentation

et al. 2015

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Abstract:The new generation of artificial satellites is providing a huge amount of Earth observation images whose exploitation can report invaluable benefits, both economical and environmental. However, only a small fraction of this data volume has been analyzed, mainly due to the large human resources needed for that task. In this sense, the development of unsupervised methodologies for the analysis of these images is a priority. In this work, a new unsupervised segmentation algorithm for satellite images is proposed. This algorithm is based on the rough-set theory, and it is inspired by a previous segmentation algorithm defined in the RGB color domain. The main contributions of the new algorithm are: (i) extending the original algorithm to four spectral bands; (ii) the concept of the superpixel is used in order to define the neighborhood similarity of a pixel adapted to the local characteristics of each image; (iii) and two new region merged strategies are proposed and evaluated in order to establish the final number of regions in the segmented image. The experimental results show that the proposed approach improves the results provided by the original method when both are applied to satellite images with different spectral and spatial resolutions.

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Discrete-Continuous Depth Estimation from a Single Image

Cited by 365 publications

References 19 publications

Fast depth extraction from a single image

Fast depth extraction from a single image

Untitled

Superpixel-Based Roughness Measure for Multispectral Satellite Image Segmentation

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