三维物体表面材质实时编辑

艺术家进行设计时,常常需要一种可以交瓦地修改模型表面材质的工具.为了在环境光下对模型表面的材质进行实时编辑,提出一种基于预计算辐射传输的算法:首先预计算环境光相对于模型表面每一个顶点的可见性;然后在绘制时实时计算物体表面的双向反射分布函数(BRDF);最后通过查找环境光相对于模型表面每一个顶点的可见性,快速绘制出物体表面材质.实验结果表明,使用该算法,用户可以通过调节BRDF的参数,实现物体表面材质的实时动态编辑,同时支持动态视点和动态环境光.     

基于BRDF 和GPU 并行计算的全局光照实时渲染

基于光线追踪,将屏幕图像像素分解为投射光线与场景对象交点面片辐射亮度和 纹理贴图的合成,每个面片的辐射亮度计算基于双向反射分布函数(BRDF)基的线性组合,并通 过图形处理器(GPU)处理核心并行绘制进行加速,最后与并行计算的纹理映射结果进行合成。 提出了一种基于BRDF 和GPU 并行计算的全局光照实时渲染算法,利用GPU 并行加速,在提 高绘制效率的前提下,实现动态交互材质的全局光照实时渲染。重点研究：对象表面对光线的 多次反射用BRDF 基的线性组合来表示,将非线性问题转换为线性问题,从而提高绘制效率; 利用GPU 并行加速,分别计算对象表面光辐射能量和纹理映射及其线性组合,进一步提高计算 效率满足实时绘制需求。     

利用采样球体提取真实感纹理

提出了一种从真实物体中提取纹理的方法.利用具有复杂纹理的参考球体作为被采样物体,计算其组成材质的BRDF(bidirectional reflectance distribution function)模型参数以及各点由不同材质构成的比例,形成一幅材质权重图.该图作为纹理映射到3D物体上后,配合BRDF模型参数进行渲染,形成一种适用于重光照(relighting)的纹理.被渲染物体可根据自身方位以及光源亮度/方位呈现出自然的光影变化,达到较为逼真的外观效果.     

基于视觉特征的材质辨别算法

针对视觉识别过程中物体材质难以被识别的问题,提出一种基于物体表面光学特征的识别算法。以样本的H,I,S分量为坐标形成HIS颜色空间,通过连接处理,使样本曲面尽量连续。利用边缘检测方法去除被检测图像中的杂质点。求出剩余像素的S分量到该曲面的平均距离,作为判断图像中物体材质和样本材质相似度的标准。实验结果证明该方法简单有效。     

基于深度学习的图像本征属性预测方法综述

真实世界的外观主要取决于场景内对象的几何形状、表面材质及光照的方向和强度等图像的本 征属性。通过二维图像预测本征属性是计算机视觉和图形学中的经典问题,对于图像三维重建、增强现实等应 用具有重要意义。然而二维图像的本征属性预测是一个高维的、不适定的逆向问题,通过传统算法无法得到理 想结果。针对近年来随着深度学习在二维图像处理各个方面的应用,出现的大量利用深度学习对图像本征属性 进行预测的研究成果,首先介绍了基于深度学习的图像本征属性预测算法框架,分析了以获得场景反射率和阴 影图为主的本征图像预测、以获得图像中材质 BRDF 参数为主的本征属性预测及以获得图像光照相关信息为主 的本征属性预测 3 个方向的国内外研究进展并总结了各自方法的优缺点,最后指出了图像本征属性预测的研究 趋势和重点。     

变化光照的对象图像合成

光照是真实感图形绘制和许多图像应用中的一个非常重要的因素.提出了一种完全基于图像的方法来反映光照变化在绘制对图像时的影响.所提出的方法不是直接去估计对象反射模型中的参数,或是去拟合BRDF函数,而是用奇异值分解(SVD)来拟合Lambertian 对象在光照和几何朝向变化情况下的所有图像集合.其中,光线方向的解析表达可以由样本图像、基图像以及已知类对象的图像集导出,对象在新的光线方向下的图像可通过适当地线性组合基图像而有效地绘出.另外,利用对SVD系数的线性插值可以生成反映对象几何朝向和光线变化的连续变形     

辐射度场景中双向纹理函数表面的绘制

双向纹理函数(BTF)表面一般采用点采样数据来定义表面的光照属性,因而这类表面很难运用基于面片分割的辐射度方法进行绘制,提出一种将辐射度算法扩展到包括BTF表面场景的有效方法.对表面的BTF样本区域首先进行像素聚类,再在各个像素类内对视线采样方向做进一步自适应的聚类,在各个视线类内像素分别拟合一个低频光照甬数,并求它们在各个视线类内光照细节的高频光照函数.低频光照函数作为该表面区域的平均反射属性参与辐射度计算,生成场景的整体光照效果;然后利用计算的辐射度值和高频光照函数重建该表面区域的BTF材质细节.文中方法不仅取得了较高的压缩效率,而且在BTF材质表面产生了辉映等全局光照效果.最后利用硬件实现了视点快速改变时的场景绘制.     

基于多维向量模型模糊聚类的图像识别研究

从建立像素色彩空间的多维向量模型出发,采用一种改进的模糊C均值聚类算法对图像进行分割,从而得到一组图像像素空间的特征区域向量,并采用特征向量相似度计算方法计算图像相似度,进而比较两幅图像相似度大小,以达到图像识别的目的.通过实验对图像相似识别效果进行验证,实验表明,基于多维向量模型模糊聚类方法在图像识别中有一定应用价值.     

考虑到亮度变化和颜色纯度的图像分割法

提出了一种新的考虑到亮度变化和颜色纯度的图像分割方法.使用HIS颜色空间来取得象素的颜色信息.当饱和度和亮度值较低时,色彩值会非常敏感,用颜色纯度作色彩值的加权值.不但考虑了像素的属性,还考虑了像素群的属性.图像先被分成块,块的平均值和方差值作为像素群的属性.用基于块的领域扩张来进行图像分割.用向量距离和相对位置信息把小的对象合并到大的对象中.实验结果证明,该方法适用于多种图像.     

变化光照目标图像合成的球调和方法

光照是真实感绘制技术中的一个关键因素。研究了朗伯反射的球调和表示，提出了一种基于球调和函数的任意光照目标图像生成方法。该方法在给定目标基图像及其对应光照属性的基础上，通过基图像数据矩阵的奇异值分解（SVD）分离场景环境光分量、反演计算场景反射率和表面法向量等。新光照条件下绘制图像时，采用四阶球调和函数拟合入射光和朗伯反射核计算直接光照；间接光照采用分离出的基图像环境光分量拟合。实验表明：该方法能够保证绘制精确度，适合复杂光照条件下的目标图像生成。     

DeepBRDF: A Deep Representation for Manipulating Measured BRDF

Effective compression of densely sampled BRDF measurements is critical for many graphical or vision applications. In this paper, we present DeepBRDF, a deep-learning-based representation that can significantly reduce the dimensionality of measured BRDFs while enjoying high quality of recovery. We consider each measured BRDF as a sequence of image slices and design a deep autoencoder with a masked L₂ loss to discover a nonlinear low-dimensional latent space of the high-dimensional input data. Thorough experiments verify that the proposed method clearly outperforms PCA-based strategies in BRDF data compression and is more robust. We demonstrate the effectiveness of DeepBRDF with two applications. For BRDF editing, we can easily create a new BRDF by navigating on the low-dimensional manifold of DeepBRDF, guaranteeing smooth transitions and high physical plausibility. For BRDF recovery, we design another deep neural network to automatically generate the full BRDF data from a single input image. Aided by our DeepBRDF learned from real-world materials, a wide range of reflectance behaviors can be recovered with high accuracy.     

g‐BRDFs: An Intuitive and Editable BTF Representation

Measured reflection data such as the bidirectional texture function (BTF) represent spatial variation under the full hemisphere of view and light directions and offer a very realistic visual appearance. Despite its high‐dimensional nature, recent compression techniques allow rendering of BTFs in real time. Nevertheless, a still unsolved problem is that there is no representation suited for real‐time rendering that can be used by designers to modify the BTF's appearance. For intuitive editing, a set of low‐dimensional comprehensible parameters, stored as scalars, colour values or texture maps, is required. In this paper we present a novel way to represent BTF data by introducing the geometric BRDF (g‐BRDF), which describes both the underlying meso‐ and micro‐scale structure in a very compact way. Both are stored in texture maps with only a few additional scalar parameters that can all be modified at runtime and thus give the designer full control over the material's appearance in the final real‐time application. The g‐BRDF does not only allow intuitive editing, but also reduces the measured data into a small set of textures, yielding a very effective compression method. In contrast to common material representation combining heightfields and BRDFs, our g‐BRDF is physically based and derived from direct measurement, thus representing real‐world surface appearance. In addition, we propose an algorithm for fully automatic decomposition of a given measured BTF into the g‐BRDF representation.     

Template‐Based Sampling of Anisotropic BRDFs

BRDFs are commonly used to represent given materials’ appearance in computer graphics and related fields. Although, in the recent past, BRDFs have been extensively measured, compressed, and fitted by a variety of analytical models, most research has been primarily focused on simplified isotropic BRDFs. In this paper, we present a unique database of 150 BRDFs representing a wide range of materials; the majority exhibiting anisotropic behavior. Since time‐consuming BRDF measurement represents a major obstacle in the digital material appearance reproduction pipeline, we tested several approaches estimating a very limited set of samples capable of high quality appearance reconstruction. Initially, we aligned all measured BRDFs according to the location of the anisotropic highlights. Then we propose an adaptive sampling method based on analysis of the measured BRDFs. For each BRDF, a unique sampling pattern was computed, given a predefined count of samples. Further, template‐based methods are introduced based on reusing of the precomputed sampling patterns. This approach enables a more efficient measurement of unknown BRDFs while preserving the visual fidelity for the majority of tested materials. Our method exhibits better performance and stability than competing sparse sampling approaches; especially for higher numbers of samples.     

一种空间BRDF的编辑算法

双向反射分布函数（BRDF）一直是图形学中物体表面外观表现最通用的方法,空间BRDF是目前最优的一种BRDF,它只比标准RGB纹理多占用很小的存储空间,却达到了更加逼真的效果。但是,SBRDF的获取过程极为复杂和烦琐。该文提出了一种手工编辑SBRDF的算法,它不仅可以处理测量获取的SBRDF纹理,也可以利用标准的数字图像来实现SBRDF并进行更改。最后,通过实例渲染效果展示了该编辑工具的功效。     

A Composite BRDF Model for Hazy Gloss

We introduce a bidirectional reflectance distribution function (BRDF) model for the rendering of materials that exhibit hazy reflections, whereby the specular reflections appear to be flanked by a surrounding halo. The focus of this work is on artistic control and ease of implementation for real‐time and off‐line rendering. We propose relying on a composite material based on a pair of arbitrary BRDF models; however, instead of controlling their physical parameters, we expose perceptual parameters inspired by visual experiments [ VBF17 ]. Our main contribution then consists in a mapping from perceptual to physical parameters that ensures the resulting composite BRDF is valid in terms of reciprocity, positivity and energy conservation. The immediate benefit of our approach is to provide direct artistic control over both the intensity and extent of the haze effect, which is not only necessary for editing purposes, but also essential to vary haziness spatially over an object surface. Our solution is also simple to implement as it requires no new importance sampling strategy and relies on existing BRDF models. Such a simplicity is key to approximating the method for the editing of hazy gloss in real‐time and for compositing.     

可微绘制技术研究进展

可微绘制技术是当前虚拟现实、计算机图形学与计算机视觉领域研究的热点,其目标是改造计算机图形学中以光栅化或光线跟踪算法为主的真实感绘制流程,支持梯度信息回传以计算由输出图像的变化导致的输入几何、材质属性变化,通过与优化及深度学习技术等相结合支持从数据中学习绘制模型和逆向推理,是可微学习技术在计算机图形学绘制技术中的应用的具体体现,在增强/虚拟现实内容生成、三维重建、表观采集建模和逆向光学设计等领域中有广泛的应用前景。本文对可微绘制当前的发展状况进行调研,重点对该技术在真实感绘制、3维重建和表观采集建模中的研究和应用情况进行综述,并对可微绘制技术发展趋势进行展望,以期推动可微技术在学术界和产业界的进一步发展。     

An Image based Measurement System for Anisotropic Reflection

This paper introduces an image based method for measuring bidirectional reflectance distribution functions (BRDF). The measurement system uses a CCD camera connected to a workstation to capture images from the material under investigation. The evaluation of the BRDF is performed in an image processing step, gathering BRDF values with a broad range of incident and reflected angles, followed by a fitting process, where these values are approximated by a chosen reflectance model. Absolute BRDF values are obtained by the usage of a diffuse reflectance standard. It is shown that the proposed measurement system produces very reasonable results compared to accurate measurements. Thus, it is an easy and cost efficient way to measure material properties needed for physically based rendering algorithms.     

An empirical study on the utility of BRDF model parameters and topographic parameters for mapping vegetation in a semi‐arid region with MISR imagery

In this study we show that multiangle remote sensing is useful for increasing the accuracy of vegetation community type mapping in desert regions. Using images from the National Aeronautics and Space Administration (NASA) Multiangle Imaging Spectroradiometer (MISR), we compared roles played by Bidirectional Reflectance Distribution Function (BRDF) model parameters with those played by topographic parameters in improving vegetation community type classifications for the Jornada Experimental Range and the Sevilleta National Wildlife Refuge in New Mexico, USA. The BRDF models used were the Rahman–Pinty–Verstraete (RPV) model and the RossThin‐LiSparseReciprocal (RTnLS) model. MISR nadir multispectral reflectance was considered as baseline because nadir observation is the most basic remote sensing observation. The BRDF model parameters and the topographic parameters were considered as additional data. The BRDF model parameters were obtained by inversion of the RPV model and the RTnLS model against the MISR multiangle reflectance data. The results of 32 classification experiments show that the BRDF model parameters are useful for vegetation mapping; they can be used to raise classification accuracies by providing information that is not available in the spectral‐nadir domain, or from ancillary topographic parameters. This study suggests that the Moderate Resolution Imaging Spectroradiometer (MODIS) and MISR BRDF model parameter data products have great potential to be used as additional information for vegetation mapping.