浅谈3D立体电视

立体电视又称为三维电视(3D TV),准确的术语应该是Stereoscopies Television,它与现行电视的主要区别是,现行电视只传送一个平面的信息,而立体电视传送的是物体的浓度信息,它也是利用人眼的立体视觉特性来产生立体图像的.本文阐述了3D立体电视的实现方式,探讨了其未来的发展以及面临的瓶颈问题.     

基于直线光流场的三维运动和结构重建

利用直线间运动对应关系,将像素点光流的概念和定义方法应用于直线,提出了直线光流的概念,建立了求解空间物体运动参数的线性方程组,利用三幅图像21条直线的光流场,可以求得物体运动的12个参数以及空间直线坐标．但是在实际应用当中,要找出这21条直线的光流场是很困难的,因此该文提出了运用解非线性方程组的方法,只需要6条直线的光流．就可以分步求出物体的12个运动参数,并根据求得的12个运动参数和一致的图像坐标系中的直线坐标,求得空间直线的坐标,从而实现了三维场景的重建．     

建筑物三维场景重建方法

针对建筑物模型的规则性问题,基于一幅或多幅非标定图像,对建筑物结构进行多视角三维场景重建,得到360°观测模型。区别于传统三维重建方法,该方法无需先标定摄像机参数与逐点配准、不依赖深度数据和高分辨率图像。实验结果表明该方法解决了单张图像方向性感知视野受限的问题,在系统复杂度和速度上优于传统方法。     

基于层次结构的 3D 室内场景结构重组

室内场景,连同建筑和植物,是构成人类生存环境的三大重要因素。但是,目前可获取的三维室内场景并不像人们期望的那样是经过了合理性语义层面上的分割,而是存在着大量的拓扑结构上的错误。这些拓扑错误是由模型重用产生的碎片以及模型转换中导致的信息缺失等因素造成的。本文通过考虑拓扑结构关系(支撑关系和主次关系)以及几何关系(对称、对齐、相交、连接等),循环迭代地利用图割方法,构建了一个具有层次结构的场景树。该层次结构树对场景进行了层级的重整,用户可以根据需要得到不同细节层次上的有意义的语义分割,从而纠正了原有模型拓扑结构上的错误,为进一步的场景编辑提供了便利。     

水下三维场景实时成像系统

针对目前水下三维声纳实时成像系统前端信号通道多、波束形成计算量大的问题,提出一种基于现场可编程门阵列（FPGA）的水下三维场景实时成像系统。采用FPGA阵列控制多路信号同步采样,优化波束形成算法对海量数据进行并行处理,同时利用嵌入式处理器PowerPC控制系统,最终由主控PC完成三维图像实时显示。实验结果表明,该系统能够在水下200m的范围内实现分辨率为2cm的三维成像,三维图像刷新率可达20帧／秒。     

On-the-fly Curve-skeleton Computation for 3D Shapes

The curve-skeleton of a 3D object is an abstract geometrical and topological representation of its 3D shape. It maps the spatial relation of geometrically meaningful parts to a graph structure. Each arc of this graph represents a part of the object with roughly constant diameter or thickness, and approximates its centerline. This makes the curve-skeleton suitable to describe and handle articulated objects such as characters for animation. We present an algorithm to extract such a skeleton on-the-fly, both from point clouds and polygonal meshes. The algorithm is based on a deformable model evolution that captures the object's volumetric shape. The deformable model involves multiple competing fronts which evolve inside the object in a coarse-to-fine manner. We first track these fronts' centers, and then merge and filter the resulting arcs to obtain a curve-skeleton of the object. The process inherits the robustness of the reconstruction technique, being able to cope with noisy input, intricate geometry and complex topology. It creates a natural segmentation of the object and computes a center curve for each segment while maintaining a full correspondence between the skeleton and the boundary of the object.     

Factored Neural Representation for Scene Understanding

A long-standing goal in scene understanding is to obtain interpretable and editable representations that can be directly constructed from a raw monocular RGB-D video, without requiring specialized hardware setup or priors. The problem is significantly more challenging in the presence of multiple moving and/or deforming objects. Traditional methods have approached the setup with a mix of simplifications, scene priors, pretrained templates, or known deformation models. The advent of neural representations, especially neural implicit representations and radiance fields, opens the possibility of end-to-end optimization to collectively capture geometry, appearance, and object motion. However, current approaches produce global scene encoding, assume multiview capture with limited or no motion in the scenes, and do not facilitate easy manipulation beyond novel view synthesis. In this work, we introduce a factored neural scene representation that can directly be learned from a monocular RGB-D video to produce object-level neural presentations with an explicit encoding of object movement (e.g., rigid trajectory) and/or deformations (e.g., nonrigid movement). We evaluate ours against a set of neural approaches on both synthetic and real data to demonstrate that the representation is efficient, interpretable, and editable (e.g., change object trajectory). Code and data are available at: http://geometry.cs.ucl.ac.uk/projects/2023/factorednerf/ .     

基于运动优化语义分割的变分光流计算方法

针对光照变化和大位移运动等复杂场景下图像序列变分光流计算的边缘模糊与过度分割问题,文中提出基于运动优化语义分割的变分光流计算方法.首先,根据图像局部区域的去均值归一化匹配模型,构建变分光流计算能量泛函.然后,利用去均值归一化互相关光流估计结果,获取图像运动边界信息,优化语义分割,设计运动约束语义分割的变分光流计算模型....     

基于微机的主从式机器人场景三维可视化与仿真

文章提出了利用触觉传感信息和视频图像相融合来构造主从式机器人三维数据场的思路,并且在微机上实际构造了仿真环境,文章讨论了基于触觉的三维建模、坐标系转换、融合及绘制等问题,最后通过实际例子展示了三维场景的效果。     

Image-Driven Furniture Style for Interactive 3D Scene Modeling

Creating realistic styled spaces is a complex task, which involves design know-how for what furniture pieces go well together. Interior style follows abstract rules involving color, geometry and other visual elements. Following such rules, users manually select similar-style items from large repositories of 3D furniture models, a process which is both laborious and time-consuming. We propose a method for fast-tracking style-similarity tasks, by learning a furniture's style-compatibility from interior scene images. Such images contain more style information than images depicting single furniture. To understand style, we train a deep learning network on a classification task. Based on image embeddings extracted from our network, we measure stylistic compatibility of furniture. We demonstrate our method with several 3D model style-compatibility results, and with an interactive system for modeling style-consistent scenes.     

三维场景建模的全景外极面图像分析方法

文章提出由非精确摄像机运动下的图像序列建立3D环境模型的全景外极面图像方法,实现了 无特征提取的时空纹理方向精确估计、深度边界确定和遮挡恢复算法.该方法推广并结合了 外极面图像方法和全景图像方法,避免了现有运动分层方法迭代过程中的局部最小化问题,具 有计算和存储效率高、适应性强、算法鲁棒性好的优点.建立的自然景物的真实感三维环境 模型,可用于机器人全局定位的自然路标提取和真实环境虚拟再现的图像合成.     

基于材质的实时渲染场景组织技术

为了提高图形渲染程序的性能,根据统一脚本核心硬件的特征,提出一种在材质空间进行二次排序的场景组织算法.首先在硬件抽象层扩展了材质的定义;然后根据扩展的材质定义重新组织场景中的对象,再在材质空间中对物体进行二次排序;最后把排序好的对象发送给图形渲染线进行渲染输出.该算法在不改变图像渲染质量的前提下,可以极大地加快图像的渲染速度.实验结果表明,文中算法可较大地提高图形渲染程序的性能.     

面向微运动视频的三维重建

手持相机拍照瞬间, 通常手部抖动可产生画面的微小运动. 一方面微小运动蕴含了视差信息, 将有助于进行场景深度感知并可潜在应用于虚拟/增强现实和照片重定焦等领域. 另一方面, 由于极窄的基线, 图像对应点匹配过程中对噪声较为敏感, 因而从无标定的微运动视频重建场景极具挑战性. 当前处理微运动视频三维重建的主流方法由于没有考虑重建过程的不确定性, 导致算法精度较差. 本文提出一种高精度的从无标定微运动视频复原场景深度的算法, 主要包含2个关键步骤: 首先, 在自标定阶段, 提出一种视点加权的光束平差方法, 充分考虑邻域视点间由于基线不同所产生的匹配不确定性, 减少较窄基线视点的可信度, 保持自标定过程的鲁棒性; 进一步地, 提出一种基于广义全变分平滑的深度图估计方法, 抑制窄基线产生的深度图噪声的同时保持倾斜结构和精细几何特征. 本文提出的方法与当前处理微运动三维重建的主流方法在真实和合成数据集上进行了定量和定性实验, 充分验证了提出方法的有效性.     

基于地形的三维场景绘制方法

地形绘制是计算机图形学中的一个热门的话题,在游戏制作,仿真场景的构建方面都有重要的应用。文章给出了利用位图快速生成三维地形的算法。为了满足真实感的要求,使用了多纹理映射、天空盒以及简单水纹模拟技术。     

VRML/X3D场景的优化

VRML/X3D是新一代面向Web的交互式三维图形规范,由本地机上VRML的浏览器解释生成三维场景,也就是三维网页的技术规范。作者根据经验所得,从结构层次、文件压缩、源码优化和重复代码的简化等几个方面对VRML/X3D场景进行优化,压缩了VRML/X3D场景文件的大小,加快VRML/X3D三维网页的浏览速度。     

VRML／X3D场景的优化

VRML／X3D是新一代面向Web的交互式三维图形规范,由本地机上VRML的浏览器解释生成三维场景．也就是三维网页的技术规范。作者根据经验所得,从结构层次、文件压缩、源码优化和重复代码的简化等几个方面对VRML／X3D场景进行优化．压缩了VRML／X3D场景文件的大小,加快VRML／X3D三维网页的浏览速度。     

基于场景语义的3D交互体系结构

借鉴真实世界的认知心理学原理,将虚拟场景的可视表达和语义信息结合起来共同服务于用户的交互过程,多种3D交互技术被融合在一个统一的交互框架内,使复杂虚拟环境中的3D用户界面更容易被用户理解和使用.通过增强场景图的语义处理能力,建立支持高层语义的3D用户界面体系结构,3D交互系统不仅在几何层上而且还能在语义层上支持交互任务的执行.最后介绍了一个应用实例.     

Scene Flow-Based Environment 3D Digitalization for Mobile Robot Navigation

This paper presents an effective 3D digitalization technique to reconstruct an accurate and reliable 3D environment model from multi-view stereo for an environment-learning mobile robot. The novelty of this paper lies in the introduction of nonrigid motion analysis to stereo reconstruction routine. In our proposed scheme, reconstruction task is decoupled into two stages. Firstly, range depth of feature points is recovered and in turn is used for building a polygonal mesh and secondly, projection feedback on comparison views, which is generated on assumption of the established coarse mesh model, is carefully introduced to deform the primitive mesh model so as to improve its quality dramatically. The discrepancy of observation on comparison views and the corresponding predictive feedback is quantitatively evaluated by optical flow field and is employed to derive the corresponding scene flow vector field subsequently, which is then used for surface deformation. As optical flow vector field estimation outperforms traditional dense disparity for its inherent advantage of being robust to illumination change and being optimized and smoothed in global sense, the deformed surface can be improved in accuracy, which is validated by experimental results.     

Observability of 3D Motion

This paper examines the inherent difficulties in observing 3D rigid motion from image sequences. It does so without considering a particular estimator. Instead, it presents a statistical analysis of all the possible computational models which can be used for estimating 3D motion from an image sequence. These computational models are classified according to the mathematical constraints that they employ and the characteristics of the imaging sensor (restricted field of view and full field of view). Regarding the mathematical constraints, there exist two principles relating a sequence of images taken by a moving camera. One is the epipolar constraint, applied to motion fields, and the other the positive depth constraint, applied to normal flow fields. 3D motion estimation amounts to optimizing these constraints over the image. A statistical modeling of these constraints leads to functions which are studied with regard to their topographic structure, specifically as regards the errors in the 3D motion parameters at the places representing the minima of the functions. For conventional video cameras possessing a restricted field of view, the analysis shows that for algorithms in both classes which estimate all motion parameters simultaneously, the obtained solution has an error such that the projections of the translational and rotational errors on the image plane are perpendicular to each other. Furthermore, the estimated projection of the translation on the image lies on a line through the origin and the projection of the real translation. The situation is different for a camera with a full (360 degree) field of view (achieved by a panoramic sensor or by a system of conventional cameras). In this case, at the locations of the minima of the above two functions, either the translational or the rotational error becomes zero, while in the case of a restricted field of view both errors are non-zero. Although some ambiguities still remain in the full field of view case, the implication is that visual navigation tasks, such as visual servoing, involving 3D motion estimation are easier to solve by employing panoramic vision. Also, the analysis makes it possible to compare properties of algorithms that first estimate the translation and on the basis of the translational result estimate the rotation, algorithms that do the opposite, and algorithms that estimate all motion parameters simultaneously, thus providing a sound framework for the observability of 3D motion. Finally, the introduced framework points to new avenues for studying the stability of image-based servoing schemes.