单幅图像上透视场景的图像填补

Hao, Chuanyan; Chen, YaDang; Wu, Wen; Wu, Enhua

doi:10.1007/s11432-015-5359-x

Cited by 6 publications

(3 citation statements)

References 33 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Scene depth is essential for a variety of tasks, ranging from 3-D modeling and visualization to robot navigation. Many challenging computer vision problems have proven [1][2][3][4][5]31,32 to benefit from the incorporation of depth information, such as RGB-D visual odometry, 1 semantic labellings, 2 pose estimations, 3 3-D shape representation, 4 and 2.5-D object recognition. 5 In addition to parametric methods [6][7][8][9] for extracting depths, many nonparametric depth sampling approaches 10,11 have also been proposed to automatically convert monocular images into stereoscopic images with good performances.…”

Section: Introductionmentioning

confidence: 99%

Fast depth extraction from a single image

Dong

Wang

2016

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Fast depth extraction from a single image

Dong

Wang

2016

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…[68]. In these methods, depth-based foreground and background analysis can be used to guide the inpainting processing with reasonable constraints [72,73] (see an example in Fig. 8).…”

Section: Disocclusion Handlingmentioning

confidence: 99%

A survey on multiview video synthesis and editing

Zhang

2016

Tinshhua Sci. Technol.

View full text Add to dashboard Cite

Multiview video can provide more immersive perception than traditional single 2-D video. It enables both interactive free navigation applications as well as high-end autostereoscopic displays on which multiple users can perceive genuine 3-D content without glasses. The multiview format also comprises much more visual information than classical 2-D or stereo 3-D content, which makes it possible to perform various interesting editing operations both on pixel-level and object-level. This survey provides a comprehensive review of existing multiview video synthesis and editing algorithms and applications. For each topic, the related technologies in classical 2-D image and video processing are reviewed. We then continue to the discussion of recent advanced techniques for multiview video virtual view synthesis and various interactive editing applications. Due to the ongoing progress on multiview video synthesis and editing, we can foresee more and more immersive 3-D video applications will appear in the future.

show abstract

“…The 3D information of a scene can be obtained by cameras with especial hardware support [1] such as infrared scanning or others such technology. With the rapid development of computer vision [2,3], most people prefer to recover 3D information readily from 2D materials alone, such as from images [4,5] or videos [6,7], that can be taken easily with a normal camera. Certainly, video-based reconstruction is somewhat similar to image-based reconstruction, as a video is composed exactly of a sequence of images.…”

Section: Introductionmentioning

confidence: 99%

Robust dense reconstruction by range merging based on confidence estimation

Chen

Hao

et al. 2016

Sci. China Inf. Sci.

Self Cite

View full text Add to dashboard Cite

Although the stereo matching problem has been extensively studied during the past decades, automatically computing a dense 3D reconstruction from several multiple views is still a difficult task owing to the problems of textureless regions, outliers, detail loss, and various other factors. In this paper, these difficult problems are handled effectively by a robust model that outputs an accurate and dense reconstruction as the final result from an input of multiple images captured by a normal camera. First, the positions of the camera and sparse 3D points are estimated by a structure-from-motion algorithm and we compute the range map with a confidence estimation for each image in our approach. Then all the range maps are integrated into a fine point cloud data set. In the final step we use a Poisson reconstruction algorithm to finish the reconstruction. The major contributions of the work lie in the following points: effective range-computation and confidence-estimation methods are proposed to handle the problems of textureless regions, outliers and detail loss. Then, the range maps are merged into the point cloud data in terms of a confidence-estimation. Finally, Poisson reconstruction algorithm completes the dense mesh. In addition, texture mapping is also implemented as a post-processing work for obtaining good visual effects. Experimental results are presented to demonstrate the effectiveness of the proposed approach.

show abstract

单幅图像上透视场景的图像填补

Cited by 6 publications

References 33 publications

Fast depth extraction from a single image

Fast depth extraction from a single image

A survey on multiview video synthesis and editing

Robust dense reconstruction by range merging based on confidence estimation

Contact Info

Product

Resources

About