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An automatic 2d to 3d video conversion approach based on RGB-d images

MULTIMEdIA TOOLS ANd APPLICATIONS 2021年第13期80卷 19179-19201页

作者： Pan, Baiyu Zhang, Liming Yin, Hanxiong Lan, Jun Cao, Feilong Univ Macau Fac Sci & Technol Taipa Macao Peoples R China China Jiliang Univ Coll Sci Hangzhou Peoples R China

3d movies/videos have become increasingly popular in the market;however, they are usually produced by professionals. This paper presents a new technique for the automatic conversion of 2d to 3d video based on RGB-d sensors, which can be easily conducted by ordinary users. To generate a 3d image, one approach is to combine the original 2d color image and its corresponding depth map together to perform depth image-based rendering (dIBR). An RGB-d sensor is one of the inexpensive ways to capture an image and its corresponding depth map. The quality of the depth map and the dIBR algorithm are crucial to this process. Our approach is twofold. First, the depth maps captured directly by RGB-d sensors are generally of poor quality because there are many regions missing depth information, especially near the edges of objects. This paper proposes a new RGB-d sensor based depth map inpainting method that divides the regions with missing depths into interior holes and border holes. different schemes are used to inpaint the different types of holes. Second, an improved hole filling approach for dIBR is proposed to synthesize the 3d images by using the corresponding color images and the inpainted depth maps. Extensive experiments were conducted on different evaluation datasets. The results show the effectiveness of our method.

关键词： depth map depth image based rendering (dIBR) RGB-d sensor 2d to 3d video conversion

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depth Estimation and video Synthesis for 2d to 3d video conversion

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE ANd video TECHNOLOGY 2014年第1期76卷 33-46页

作者： Han, Chien-Chih Hsiao, Hsu-Feng Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

With the recent progress of multi-view devices and the corresponding signal processing techniques, stereoscopic viewing experience has been introduced to the public with growing interest. To create depth perception in human vision, two different video sequences in binocular vision are required for viewers. Those videos can be either captured by 3d-enabled cameras or synthesized as needed. The primary contribution of this paper is to establish two transformation models for stationary scenes and non-stationary objects in a given view, respectively. The models can be used for the production of corresponding stereoscopic videos as a viewer would have seen at the original event of the scene. The transformation model to estimate the depth information for stationary scenes is based on the information of the vanishing point and vanishing lines of the given video. The transformation model for non-stationary regions is the result of combining the motion analysis of the non-stationary regions and the transformation model for stationary scenes to estimate the depth information. The performance of the models is evaluated using subjective 3d video quality evaluation and objective quality evaluation on the synthesized views. Performance comparison with the ground truth and a famous multi-view video synthesis algorithm, VSRS, which requires six views to complete synthesis, is also presented. It is shown that the proposed method can provide better perceptual 3d video quality with natural depth perception.

关键词： View synthesis 2d to 3d video conversion Vanishing point Motion analysis

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An H.264-based Scheme for 2d to 3d video conversion

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2009年第2期55卷 742-748页

作者： Pourazad, M. T. Nasiopoulos, P. Ward, R. K. Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

An efficient method that converts 2d video sequences to 3d is presented. This method utilizes the motion information between consecutive frames to approximate the depth map of the scene. To estimate the depth map, the horizontal motion captured by a single camera is revised and then approximated as the displacement between the right and left frames captured by two cameras in a stereoscopic set-up case. To enhance the visual depth perception, a non-linear scaling model is then applied to the modified motion vectors. The low complexity of our approach and its compatibility with future 3d systems, allows real-time implementations at the receiver-end for no additional burden on the network. Performance evaluations show that our approach outperforms the existing H.264-based depth map estimation technique by 1.84 dB PSNR, providing more realistic depth representation of the scene. Moreover, the subjective comparison of results (obtained by viewers watching the generated stereo video sequences on a 3d display system) confirms the better performance of our method(1).

关键词： 2d to 3d video conversion motion-based depth map approximation stereoscopic TV 3d TV

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Low-complexity 2d to 3d video conversion

Low-complexity 2D to 3D Video Conversion

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Conference on Stereoscopic displays and Applications XXII

作者： Chen, Ying Zhang, Rong Karczewicz, Marta Qualcomm Inc QCT Multimedia R&D & Stand San Diego CA 92121 USA

ISBN: (纸本)9780819484000

3d film and 3d TV are becoming reality. More facilities and devices are now 3d capable. Compared to capture 3d video content directly, 2d to 3d video conversion is a low-cost, backward compatible alternate. There also exists a tremendous amount of monoscopic 2d video content that are of high interest to be displayed on 3d devices with noticeable immersiveness. 2d to 3d video conversion, therefore, has drawn lots of attention recently. In this paper, a low complexity 2d to 3d conversion algorithm is presented. The conversion generates stereo video pairs by 3d warping based on estimated per-pixel depth maps. The depth maps are estimated jointly by motion and color cues. Subjective tests show that the proposed algorithm achieves 3d perception with acceptable artifact.

关键词： 3d video 2d to 3d video conversion depth estimation view synthesis

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AN EFFICIENT 2d to 3d video conversion METHOd BASEd ON SKELETON LINE TRACKING

AN EFFICIENT 2D TO 3D VIDEO CONVERSION METHOD BASED ON SKELE...

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3dTV Conference - True Vision - Capture, Transmission and display of 3d video

作者： Li, Zheng Xie, Xudong Liu, Xiaodong Tsinghua Univ Dept Automat TNList Beijing 100084 Peoples R China Tsinghua Univ Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9781424443178

3dTV is becoming more and more popular but the 3d sequences are still very few. In this paper, we proposed an efficient 2d to 3d video conversion method based on skeleton line tracking. In our method, for a key frame, the foreground object and the corresponding depth image can be obtained by interactive method. For a non-key frame, we first generate the skeleton lines of the object in the previous frame and predict them in the current frame, then recover the object by the Lazy Snapping method. A robust and fast optical flow method is introduced to make the prediction better. At last, the depth image is generated to composite the stereo image. Because only the skeleton lines of the object rather than the whole object are tracked, the computational complexity is much lower than other tracking methods. The experimental results show that our method is feasible.

关键词： 2d to 3d video conversion skeleton line tracking Lazy Snapping

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Robust Semi-Automatic depth Map Generation in Unconstrained Images and video Sequences for 2d to Stereoscopic 3d conversion

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IEEE TRANSACTIONS ON MULTIMEdIA 2014年第1期16卷 122-136页

作者： Phan, Raymond Androutsos, dimitrios Ryerson Univ Dept Elect & Comp Engn Toronto ON M5B 2K3 Canada

We describe a system for robustly estimating synthetic depth maps in unconstrained images and videos, for semi-automatic conversion into stereoscopic 3d. Currently, this process is automatic or done manually by rotoscopers. Automatic is the least labor intensive, but makes user intervention or error correction difficult. Manual is the most accurate, but time consuming and costly. Noting the merits of both, a semi-automatic method blends them together, allowing for faster and accurate conversion. This requires user-defined strokes on the image, or over several keyframes for video, corresponding to a rough estimate of the depths. After, the rest of the depths are determined, creating depth maps to generate stereoscopic 3d content, with depth Image Based Rendering to generate the artificial views. depth map estimation can be considered as a multi-label segmentation problem: each class is a depth. For video, we allow the user to label only the first frame, and we propagate the strokes using computer vision techniques. We combine the merits of two well-respected segmentation algorithms: Graph Cuts and Random Walks. The diffusion from Random Walks, with the edge preserving of Graph Cuts should give good results. We generate good quality content, more suitable for perception, compared to a similar framework.

关键词： Computer vision depth maps graph cuts image segmentation motion estimation object tracking random walks semi-automatic 2d to 3d image conversion 2d to 3d video conversion

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Unconstrained 2d to Stereoscopic 3d Image and video conversion Using Semi-Automatic Energy Minimization Techniques

Unconstrained 2D to Stereoscopic 3D Image and Video Conversi...

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SMPTE Technical Conference

作者： Raymond Phan Richard Rzeszutek dimitrios Androutsos Department of Electrical & Computer Engineering Ryerson University 350 Victoria St. Toronto ON M5B 2K3 Canada

ISBN: (纸本)9781622765386

We present a method for semi-automatically converting unconstrained 2d images and video content into stereoscopic 3d. The user is presented with the image to convert, and brushes user-defined depth strokes in certain areas. These correspond to a rough estimate of the scene depths within these points. After, the rest of the depths are solved using this information, producing a depth map to create stereoscopic 3d content. For video, the user chooses several keyframes for brushing, and the depths for the entire video are found in a volumetric basis. Additionally for video, the user has the option of minimizing effort by employing a robust tracking algorithm, where the first frame only needs to be labeled. After, the labels are propagated throughout the entire video, ultimately increasing accuracy with more frames labeled. Our work combines the merits of two energy minimization techniques: Graph Cuts and Random Walks. The former respects boundaries, but does not have suitable depth diffusion, making the scene look like “cardboard cutouts”. The latter has good depth diffusion, but object boundaries are blurred. Therefore, combining the merits of both will lead to a higher quality result. Current efforts rely on automatic or manual conversion by rotoscopers. The former prohibits user intervention, while the latter is time consuming, prohibiting use in smaller studios. Semi-automatic is a compromise to allow for more faster and accurate conversion, decreasing the time for studios to release 3d content. The results shown in this paper generate good quality stereoscopic depth maps with minimal effort required.

关键词： 2d to 3d Image conversion 2d to 3d video conversion Random Walks Graph Cuts depth Maps depth-Label Tracking Stereoscopy Semi-Automatic Image Segmentation Stereoscopic Stereo image processing Studios videotapes two dimentional images Magneto electrical resistivity imaging technique brushing Three-dimensional Images

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depth propagation for semi-automatic 2d to 3d conversion

Depth propagation for semi-automatic 2D to 3D conversion

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Conference on Three-dimensional Image Processing, Measurement (3dIPM), and Applications

作者： Tolstaya, Ekaterina Pohl, Petr Rychagov, Michael Samsung R&D Inst Russia Moscow 125009 Russia

ISBN: (纸本)9781628414837

In this paper, we present a method for temporal propagation of depth data that is available for so called key-frames through video sequence. Our method requires that full frame depth information is assigned. Our method utilizes nearest preceding and nearest following key-frames with known depth information. The propagation of depth information from two sides is essential as it allows to solve most occlusion problems correctly. Image matching is based on the coherency sensitive hashing (CSH) method and is done using image pyramids. disclosed results are compared with temporal interpolation based on motion vectors from optical flow algorithm. The proposed algorithm keeps sharp depth edges of objects even in situations with fast motion or occlusions. It also handles well many situations, when the depth edges don't perfectly correspond with true edges of objects.

关键词： 2d to 3d video conversion video tracking depth interpolation patch hashing

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A TECHNIQUE FOR FAKE 3d (2d-TO-3d CONVERTEd) video RECOGNITION 23

A TECHNIQUE FOR FAKE 3D (2D-TO-3D CONVERTED) VIDEO RECOGNITI...

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23rd European Signal Processing Conference (EUSIPCO)

作者： Kakaletsis, Efstratios Nikolaidis, Nikos Aristotle Univ Thessaloniki Dept Informat Artificial Intelligence & Informat Anal Lab Box 451 GR-54124 Thessaloniki Greece

ISBN: (纸本)9780992862633

In this paper, we propose a technique for the automatic recognition of "fake" stereoscopic videos/movies i.e., videos which result from classic 2d videos through a 2d to 3d conversion process. Essentially, the proposed technique distinguishes between 2d movies converted to 3d and real stereoscopic ones. It is based on the difference in sharpness around foreground objects in a converted stereo frame pair caused from the in painting step that takes place after the generation of the right frame (rendered view) from the left frame (source view), The two variants of the algorithm, one utilizing a two-class Support Vector Machine and another one that follows a threshold based classification approach, use a sharpness metric evaluated on a stripe created around foreground objects such as human figures. Experimental evaluation of the proposed algorithm, which can serve as 3d quality characterization tool, is conducted on several stereoscopic movies with very promising results.

关键词： 3dTV 3d cinema stereoscopic video Real 3d video Fake 3d video 2d to 3d video conversion quality assessment

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Multi-Stage Feature Pyramid Stereo Network-Based disparity Estimation Approach for Two to Three-dimensional video conversion

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IEEE TRANSACTIONS ON CIRCUITS ANd SYSTEMS FOR video TECHNOLOGY 2021年第5期31卷 1862-1875页

作者： Pan, Baiyu Zhang, Liming Wang, Hanzi Univ Macau Fac Sci & Technol Taipa Macau Peoples R China Xiamen Univ Sch Informat Xiamen 361005 Peoples R China

disparity estimation is a popular topic in computer vision and has drawn increasing attention in recent years. In this article, we propose a new multi-stage network for the purpose of two to three-dimensional video conversion that contains two training stages: an initial disparity estimation as the first training stage and depth-image-based rendering (dIBR) as an extra component to form the second training stage. In the first training stage, we propose a revised end-to-end feature pyramid stereo network, in which the original non-pyramid structure is replaced by a bottom-up convolutional neural network pyramid for disparity regression. It utilizes the spatial information by concatenating different scale features to boost the performance on boundary consistency. Mirror connections between feature extraction and disparity regression on the corresponding layers are also added to improve the quality of the results. In the second stage, we propose an improved disocclusion filling technique in the dIBR branch and connect the non-neural-network method to the disparity estimation network. This two-stage training strategy can work effectively to generate the improved disparity estimation for two to three-dimensional video conversion. Extensive experiments are conducted and some selected state-of-the-art algorithms are compared with our proposed approach on the popular KITTI2015 and Scene Flow datasets. The results demonstrate that our estimated disparity map can generate high quality 3d images.

关键词： Three-dimensional displays Estimation Feature extraction Two dimensional displays Training Neural networks TV 2d to 3d video conversion neural network deep learning disparity estimation feature pyramid depth image based rendering (dIBR)

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