Image-based rendering of range data with estimated depth uncertainty

Image-based rendering (IBR) involves constructing an image from a new viewpoint, using several input images from different viewpoints. Our approach is to acquire or estimate the depth for each pixel of each input image. We then reconstruct the new view from the resulting collection of 3D points. When rendering images from photographs, acquiring and registering data is far from perfect. Accuracy can fluctuate, depending on the choice of geometry reconstruction technique. Our image-rendering approach involves three steps: depth extraction, uncertainty estimation, and rendering. That is, we first compute a depth map for every input image. Then we calculate the uncertainty information using the estimated depth maps as starting points. Finally, we perform the actual rendering, which renders the uncertainty estimated in the previous step as ellipsoidal Gaussian splats.     

3D遮挡模型引导的光场图像深度获取

目的 光场相机可以通过单次曝光同时从多个视角采样单个场景,在深度估计领域具有独特优势。消除遮挡的影响是光场深度估计的难点之一。现有方法基于2D场景模型检测各视角遮挡状态,但是遮挡取决于所采样场景的3D立体模型,仅利用2D模型无法精确检测,不精确的遮挡检测结果将降低后续深度估计精度。针对这一问题,提出了3D遮挡模型引导的光场图像深度获取方法。方法 向2D模型中的不同物体之间添加前后景关系和深度差信息,得到场景的立体模型,之后在立体模型中根据光线的传输路径推断所有视角的遮挡情况并记录在遮挡图（occlusion map）中。在遮挡图引导下,在遮挡和非遮挡区域分别使用不同成本量进行深度估计。在遮挡区域,通过遮挡图屏蔽被遮挡视角,基于剩余视角的成像一致性计算深度;在非遮挡区域,根据该区域深度连续特性设计了新型离焦网格匹配成本量,相比传统成本量,该成本量能够感知更广范围的色彩纹理,以此估计更平滑的深度图。为了进一步提升深度估计的精度,根据遮挡检测和深度估计的依赖关系设计了基于最大期望（exception maximization,EM）算法的联合优化框架,在该框架下,遮挡图和深度图通过互相引导的方式相继提升彼此精度。结果 实验结果表明,本文方法在大部分实验场景中,对于单遮挡、多遮挡和低对比度遮挡在遮挡检测和深度估计方面均能达到最优结果。均方误差（mean square error,MSE）对比次优结果平均降低约19.75%。结论 针对遮挡场景的深度估计,通过理论分析和实验验证,表明3D遮挡模型相比传统2D遮挡模型在遮挡检测方面具有一定优越性,本文方法更适用于复杂遮挡场景的深度估计。     

Depth‐map generation for multi‐view autostereoscopic 3‐D displays based on the SIFT algorithm constrained by boundary

Abstract— A depth‐map estimation method, which converts two‐dimensional images into three‐dimensional (3‐D) images for multi‐view autostereoscopic 3‐D displays, is presented. The proposed method utilizes the Scale Invariant Feature Transform (SIFT) matching algorithm to create the sparse depth map. The image boundaries are labeled by using the Sobel operator. A dense depth map is obtained by using the Zero‐Mean Normalized Cross‐Correlation (ZNCC) propagation matching method, which is constrained by the labeled boundaries. Finally, by using depth rendering, the parallax images are generated and synthesized into a stereoscopic image for multi‐view autostereoscopic 3‐D displays. Experimental results show that this scheme achieves good performances on both parallax image generation and multi‐view autostereoscopic 3‐D displays.     

遮挡场景的光场图像深度估计方法

光场相机通过单次拍摄可获取立体空间中的4维光场数据,利用光场的多视角特性可从中提取全光场图像的深度信息.然而,现有深度估计方法很少考虑场景中存在遮挡的情况,当场景中有遮挡时,提取深度信息的精度会明显降低.对此,提出一种新的基于多线索融合的光场图像深度提取方法以获取高精度的深度信息.首先分别利用自适应散焦算法和自适应匹配算法提取场景的深度信息;然后用峰值比作为置信以加权融合两种算法获取的深度;最后,用具有结构一致性的交互结构联合滤波器对融合深度图进行滤波,得到高精度深度图.合成数据集和真实数据集的实验结果表明,与其他先进算法相比,所提出的算法获取的深度图精度更高、噪声更少、图像边缘保持效果更好.     

Direct light field rendering without 2D image generation

Rapid developments in 3D display technologies have enabled consumers to enjoy 3D environments in an increasingly immersive manner through various display systems such as stereoscopic, multiview, and light field displays. However, there is a corresponding increase in the complexity of the conventional multiview rendering process in the attempt to achieve a sufficient level of reality, which may hinder the further commercial viability of 3D display products based on such a conventional approach. This paper proposes a novel method, the so‐called direct light field rendering, which can compose the display 3D panel image without reconstructing all the multiview images beforehand. Interpreting the 3D display as sampling in the light field domain, we attempt to directly compute only the necessary samples, not the entire light fields or multiview images. Our proposed algorithm involves the solving of linear systems of two variables, thereby requiring remarkably low computational complexities. Experimental results show that the computation time and memory usage remain as little as 12% and 1% of those required by the conventional one.     

Silhouette‐Aware Warping for Image‐Based Rendering

Image‐based rendering (IBR) techniques allow capture and display of 3D environments using photographs. Modern IBR pipelines reconstruct proxy geometry using multi‐view stereo, reproject the photographs onto the proxy and blend them to create novel views. The success of these methods depends on accurate 3D proxies, which are difficult to obtain for complex objects such as trees and cars. Large number of input images do not improve reconstruction proportionally; surface extraction is challenging even from dense range scans for scenes containing such objects. Our approach does not depend on dense accurate geometric reconstruction; instead we compensate for sparse 3D information by variational image warping. In particular, we formulate silhouette‐aware warps that preserve salient depth discontinuities. This improves the rendering of difficult foreground objects, even when deviating from view interpolation. We use a semi‐automatic step to identify depth discontinuities and extract a sparse set of depth constraints used to guide the warp. Our framework is lightweight and results in good quality IBR for previously challenging environments.     

基于投票决策的实时遮挡处理技术

针对当前基于深度信息的虚实遮挡处理技术面临的实时性差和精度低的问题,提出一种基于局部区域深度估计和基于patch相似性噪声点投票融合的实时虚实遮挡处理算法.该算法将真实场景视频序列作为输入,首先利用局部区域深度估计算法通过稀疏重建估算出稀疏关键点的深度信息,对稀疏深度施加目标区域的约束限制深度向周围像素的传播,从而快速...     

Circular camera array system for 3‐D displays based on the reconstruction of parallax rays

Abstract— A circular camera system employing an image‐based rendering technique that captures light‐ray data needed for reconstructing three‐dimensional (3‐D) images by using reconstruction of parallax rays from multiple images captured from multiple viewpoints around a real object in order to display a 3‐D image of a real object that can be observed from multiple surrounding viewing points on a 3‐D display is proposed. An interpolation algorithm that is effective in reducing the number of component cameras in the system is also proposed. The interpolation and experimental results which were performed on our previously proposed 3‐D display system based on the reconstruction of parallax rays will be described. When the radius of the proposed circular camera array was 1100 mm, the central angle of the camera array was 40°, and the radius of a real 3‐D object was between 60 and 100 mm, the proposed camera system, consisting of 14 cameras, could obtain sufficient 3‐D light‐ray data to reconstruct 3‐D images on the 3‐D display.     

单幅自然场景深度恢复

离焦测距算法是一种用于恢复场景深度信息的常用算法。传统的离焦测距算法通常需要采集多幅离焦图像,实际应用中具有很大的制约性。文中基于局部模糊估计提出单幅离焦图像深度恢复算法。基于局部模糊一致性的假设,本文采用简单而有效的两步法恢复输入图像的深度信息：1)通过求取输入离焦图和利用已知高斯核再次模糊图之间的梯度比得到边缘处稀疏模糊图 2)将边缘位置模糊值扩离至全部图像,完整的相对深度信息即可恢复。为了获得准确的场景深度信息,本文加入几何条件约束、天空区域提取策略来消除颜色、纹理以及焦点平面歧义性带来的影响,文中对各种类型的图片进行对比实验,结果表明该算法能在恢复深度信息的同时有效抑制图像中的歧义性。     

Quality assessment of 3D synthesized images based on structural and textural distortion

Depth image based rendering (DIBR) is a popular technique for rendering virtual 3D views in stereoscopic and autostereoscopic displays. The quality of DIBR-synthesized images may decrease due to various factors, e.g., imprecise depth maps, poor rendering techniques, inaccurate camera parameters. The quality of synthesized images is important as it directly affects the overall user experience. Therefore, the need arises for designing algorithms to estimate the quality of the DIBR-synthesized images. The existing 2D image quality assessment metrics are found to be insufficient for 3D view quality estimation because the 3D views not only contain color information but also make use of disparity to achieve the real depth sensation. In this paper, we present a new algorithm for evaluating the quality of DIBR generated images in the absence of the original references. The human visual system is sensitive to structural information; any deg radation in structure or edges affects the visual quality of the image and is easily noticeable for humans. In the proposed metric, we estimate the quality of the synthesized view by capturing the structural and textural distortion in the warped view. The structural and textural information from the input and the synthesized images is estimated and used to calculate the image quality. The performance of the proposed quality metric is evaluated on the IRCCyN IVC DIBR images dataset. Experimental evaluations show that the proposed metric outperforms the existing 2D and 3D image quality metrics by achieving a high correlation with the subjective ratings.

     

Depth map inpainting via sparse distortion model

The depth map captured from a real scene by the Kinect motion sensor is always influenced by noise and other environmental factors. As a result, some depth information is missing from the map. This distortion of the depth map directly deteriorates the quality of the virtual viewpoints rendered in 3D video systems. We propose a depth map inpainting algorithm based on a sparse distortion model. First, we train the sparse distortion model using the distortion and real depth maps to obtain two learning dictionaries: one for distortion and one for real depth maps. Second, the sparse coefficients of the distortion and the real depth maps are calculated by orthogonal matching pursuit. We obtain the approximate features of the distortion from the relationship between the learning dictionary and the sparse coefficients of the distortion map. The noisy images are filtered by the joint space structure filter, and the extraction factor is obtained from the resulting image by the extraction factor judgment method. Finally, we combine the learning dictionary and sparse coefficients from the real depth map with the extraction factor to repair the distortion in the depth map. A quality evaluation method is proposed for the original real depth maps with missing pixels. The proposed method achieves better results than comparable methods in terms of depth inpainting and the subjective quality of the rendered virtual viewpoints.     

任意视角的多视图立体匹配系统

为了得到高精度深度图,通过特征点提取与匹配、计算基础矩阵F、引导互匹配、捆集调整等一系列技术,求出每幅视图在同一空间坐标系下的高精度摄像机矩阵P.为任意角度的多个视图建立可扩展的主框架,不需要图像矫正.基于图像坐标和自动计算出的视差范围建立三维网络,为每条边分配适当的权值,把多视图立体匹配问题转化为网络最大流问题.算法保证了高准确率和平滑性,实验结果表明此方法适用于三维模型重建.     

Optimizing stereo‐to‐multiview conversion for autostereoscopic displays

We present a novel stereo‐to‐multiview video conversion method for glasses‐free multiview displays. Different from previous stereo‐to‐multiview approaches, our mapping algorithm utilizes the limited depth range of autostereoscopic displays optimally and strives to preserve the scene's artistic composition and perceived depth even under strong depth compression. We first present an investigation of how perceived image quality relates to spatial frequency and disparity. The outcome of this study is utilized in a two‐step mapping algorithm, where we (i) compress the scene depth using a non‐linear global function to the depth range of an autostereoscopic display and (ii) enhance the depth gradients of salient objects to restore the perceived depth and salient scene structure. Finally, an adapted image domain warping algorithm is proposed to generate the multiview output, which enables overall disparity range extension.     

面向三维视频的虚拟视点合成技术研究进展

基于深度图像的虚拟视点合成技术是三维视频信息处理、虚拟现实和计算机图形学领域的新兴交叉技术。介绍了三维视频虚拟视点绘制（或合成）技术的原理;分别就虚拟视点合成过程产生的重叠、伪影、空洞三种问题的产生机理进行分析总结;分类综述了空洞填补中基于背景重建的方法、基于图像补全的方法和基于相关图像的修复方法,并列举了提升合成图像质量的方法。总结并展望了深度学习等技术是虚拟视点合成技术的未来研究方向。     

Viewpoint image generation for head tracking 3D display using multi‐camera and approximate depth information

A simple and high image quality method for viewpoint image synthesis from multi‐camera images for a stereoscopic 3D display using head tracking is proposed. In this method, slices of images for depth layers are made using approximate depth information, the slices are linearly blended corresponding to the distance between the viewpoint and cameras at each layer, and the layers are overlaid from the perspective of viewpoint. Because the linear blending automatically compensates for depth error because of the visual effects of depth‐fused 3D (DFD), the resulting image is natural to observer's perception. Smooth motion parallax of wide depth range objects induced by viewpoint movement for left‐and‐right and front‐and‐back directions is achieved using multi‐camera images and approximate depth information. Because the calculation algorithm is very simple, it is suitable for real time 3D display applications.     

Virtual Video Camera: Image‐Based Viewpoint Navigation Through Space and Time

We present an image‐based rendering system to viewpoint‐navigate through space and time of complex real‐world, dynamic scenes. Our approach accepts unsynchronized, uncalibrated multivideo footage as input. Inexpensive, consumer‐grade camcorders suffice to acquire arbitrary scenes, for example in the outdoors, without elaborate recording setup procedures, allowing also for hand‐held recordings. Instead of scene depth estimation, layer segmentation or 3D reconstruction, our approach is based on dense image correspondences, treating view interpolation uniformly in space and time: spatial viewpoint navigation, slow motion or freeze‐and‐rotate effects can all be created in the same way. Acquisition simplification, integration of moving cameras, generalization to difficult scenes and space–time symmetric interpolation amount to a widely applicable virtual video camera system.     

Hallucinating Stereoscopy from a Single Image

We introduce a novel method for enabling stereoscopic viewing of a scene from a single pre‐segmented image. Rather than attempting full 3D reconstruction or accurate depth map recovery, we hallucinate a rough approximation of the scene's 3D model using a number of simple depth and occlusion cues and shape priors. We begin by depth‐sorting the segments, each of which is assumed to represent a separate object in the scene, resulting in a collection of depth layers. The shapes and textures of the partially occluded segments are then completed using symmetry and convexity priors. Next, each completed segment is converted to a union of generalized cylinders yielding a rough 3D model for each object. Finally, the object depths are refined using an iterative ground fitting process. The hallucinated 3D model of the scene may then be used to generate a stereoscopic image pair, or to produce images from novel viewpoints within a small neighborhood of the original view. Despite the simplicity of our approach, we show that it compares favorably with state‐of‐the‐art depth ordering methods. A user study was conducted showing that our method produces more convincing stereoscopic images than existing semi‐interactive and automatic single image depth recovery methods.     

基于非参数化采样的单幅图像深度估计

针对传统单幅图像深度估计线索不足及深度估计精度不准的问题,提出一种基于非参数化采样的单幅图像深度估计方法。该方法利用非参数化的学习手段,将现有RGBD数据集中的深度信息迁移到输入图像中去。首先计算输入图像和现有RGBD数据集多尺度的高层次图像特征;然后,在现有RGBD数据集中,基于高层次的图像特征通过kNN最近邻搜索找到若干与输入图像特征最匹配的候选图像,并将这些候选图像对通过SIFT流形变到输入图像进行对齐。最后,对候选深度图进行插值和平滑等优化操作便可以得到最后的深度图。实验结果表明,与现有算法相比,该方法估计得到的深度图精度更高,对输入图像的整体结构保持得更好。     

Digital shallow depth-of-field adapter for photographs

This paper proposes a novel method to synthesize shallow depth-of-field images from two input photographs taken with different aperture values. The basic approach is to estimate the depth map of a given scene using a DFD (depth-from-defocus) algorithm and blur an input image according to the estimated depth map. The depth information estimated by DFD contains much noise and error, while the estimation is rather accurate along the edges of the image. To overcome the limitation, we propose a depth map filling algorithm using a set of initial depth maps and a segmented image. After depth map filling, the depth map can be fine tuned by applying segment clustering and user interaction. Since our method blurs an input image according to the estimated depth information, it generates physically plausible result images with shallow depth-of-field. In addition to depth-of-field control, the proposed method can be utilized for digital refocusing and detail control in image stylization.     

虚平面映射:深度图像内部视点的绘制技术

首先推导与归纳了图像三维变换中像素深度场的变换规律,同时提出了基于深度场和极线原则的像素可见性别方法,根据上述理论和方法,提出一种基于深度图像的建模与绘制(image-based modeling and rendering,简称IBMR)技术,称为虚平面映射.该技术可以基于图像空间内任意视点对场景进行绘制.绘制时,先在场景中根据视线建立若干虚拟平面,将源深度图像中的像素转换到虚平面上,然后通过对虚平面上像素的中间变换,将虚平面转换成平面纹理,再利用虚平面的相互拼接,将视点的成像以平面纹理映射的方式完成.新方法还能在深度图像内侧,基于当前视点快速获得该视点的全景图,从而实现视点的实时漫游.新方法视点运动空间大、存储需求小,且可以发挥图形硬件的纹理映射功能,并能表现物体表面的三维凹凸细节和成像视差效果,克服了此前类似算法的局限和不足.