渭南网站建设seo,沉默是金女声翻唱,恩施建站建设,专业精准网络营销推广文章目录 一、基本信息1.1 论文基本信息1.2 课程基本信息1.3 博文基本信息 二、论文评述#xff08;中英双语#xff09;2.1 研究问题#xff08;Research Problem#xff09;2.2 创新点#xff08;Innovation/Contribution#xff09;2.3 优点#xff08;Why this pape… 文章目录 一、基本信息1.1 论文基本信息1.2 课程基本信息1.3 博文基本信息 二、论文评述中英双语2.1 研究问题Research Problem2.2 创新点Innovation/Contribution2.3 优点Why this paper is well written2.4 不足Inadequacies 一、基本信息
1.1 论文基本信息
标题Neural Layered BRDFs 来源ACM SIGGRAPH 2022中国计算机学会CCF推荐国际学术会议-计算机图形学与多媒体-A类 作者单位南京理工大学、南开大学、Adobe Research、加利福尼亚大学 原文https://dl.acm.org/doi/10.1145/3528233.3530732 小组报告时间2023年上半年本人为小组组员
1.2 课程基本信息
课程名称科技论文写作
开课单位浙江工业大学计算机学院
课程性质硕士课程-专业课-核心课程
先修课程硕士英语、机器学习等
教学目的使学生在学习了专业课程并经历了一定的科研项目试验过程的基础上了解科技论文的写作目的、掌握其写作的基本过程和规则从而提高研究生的科技论文写作效率。
课程思政元素实事求是、精益求精、突破陈规。本课程鼓励学生写论文要符合三真即真问题、真方法、真数据以实事求是的态度撰写科技论文同时写作的过程中对所提的每个观点进行自我发问做到精益求精最后还要对所提问题和方法进行深入思考是否能够突破陈规体现创新性。
课程大纲 研究问题确认 1.1 讲逻辑要先区分事实与观点 1.2 研究是什么研究者如何看待 1.3 从研究主题到具体问题 1.4 找到有用的文献 1.5 与文献交互如何草拟研究论文初稿 2.1 规划论文思路 2.2 论文初稿设计 2.3 草拟论文 2.4 论文写作工具如何修订论文 3.1 如何选择表格和图形呈现论据 3.2 草稿的修订 3.3 拟出最终的引言和结论 3.4 修订句子 3.5 从论文评语中学习如何做报告与科研精神 4.1 设计口头报告 4.2 口头报告要适合聆听 4.3 关于科研精神 教学参考资料《芝加哥大学论文写作指南》
考核方式课内考察采取课堂讲授、专题学术报告、讨论、课程报告相结合的教学方式
1.3 博文基本信息
本课程要求学生组成小组在选择的研究领域中选择来自顶会顶刊的论文进行阅读。小组内的每位成员必须对论文发表自己的见解向组长提交一份书面报告由组长总结所有小组成员的观点使用PPT口头报告的方式进行展示。
本小组选择的研究领域为计算机图形学Computer GraphicsCG它是一种使用数学算法将二维或三维图形转化为计算机表示的科学。其主要研究内容是如何在计算机中表示图形进而利用计算机进行图形的计算、处理和显示。计算机图形学的核心目标在于创建有效的视觉交流在科学、娱乐等领域和艺术创作、商业广告、产品设计等行业中发挥着重要作用。 以下部分来自博主个人的书面报告该报告形成过程中经过小组交流讨论但由于本人研究方向并不是计算机图形学实际上是以大同行的视角对论文进行评述一家之言仅供参考。以下部分为终稿若无特殊情况将不再进行修改。
二、论文评述中英双语
2.1 研究问题Research Problem
在计算机图形学中双向反射分布函数BRDF被广泛用于表示和渲染多层材料。然而现有评估方法存在高方差、高成本、精确性低等问题。
In computer graphics, Bidirectional Reflectance Distribution Functions (BRDFs) are pervasively used to represent and render layered materials. However, existing methods have the limitations of high variance, high cost and less accuracy.
2.2 创新点Innovation/Contribution
作者提出用神经网络将BRDF压缩为潜在表示在神经空间中进行分层并通过分层网络对这些潜在向量执行学习的分层操作。与最先进的方法相比本文提出的BRDF评估方法具有无噪声和计算效率高的特点。
The authors proposed to perform layering in the neural space by compressing BRDFs into latent codes via a proposed representation neural network and performing a learned layering operation on these latent vectors via a layering network. The proposed method is noise-free and computationally efficient compared to the state-of-the-art approach.
2.3 优点Why this paper is well written
1摘要部分简练而全面覆盖全文要点介绍、方法、实验结果、结论。(The abstract is terse while comprehensive, covering the full text points: introduction, methods, experiments and conclusion. )
2文章结构框架合理第3节、第4节的小节标题与大节标题相互对应。(The paper has a reasonable structure or framework. For instance, the subtitles of the third and forth section correspond to the titles of these sections.)
3引言部分逻辑清晰。第一段简明扼要地介绍了研究问题的背景以从一般到特定的顺序明确主题并举例说明应用场景第二段和第三段分别对解决当前问题的旧方法和解决更简单问题的新方法进行了陈述和评价在第四段提出自己的主张声明了本文的贡献为读者提供了清晰的导向。(The logic of the introduction is clear. The first paragraph introduces the background briefly, identifies the topic in a way from general to specific, and puts up several typical application scenarios. The second and third paragraph state and evaluate the old methods of current problems and the new methods of simpler problems, respectively. The fourth paragraph offers contribution claims, providing a clear guidance for the reader.)
4在相关工作部分按照多个类别进行组织。对前人的工作进行了充分论述介绍了研究的来龙去脉比较他们的差异并进行归类并与本文的方法比较突出本文的贡献。其中特别提到了当前的真相方法通过声明本文的方法接近真相为本文方法的有效性提供了逻辑上的有力支持。(The related work is organized in several categories. Previous work is fully discussed, the context of the study is presented, their differences are compared and used for classification. The contribution of the article is highlighted by comparing with these classified methods. Among them, the ground-truth is particularly mentioned, providing a logically strong support for the effectiveness of the proposed method. )
5在第三节的开头用一段话简要介绍了这一节的内容。在3.1节用公式对问题进行了描述。这里的formulate用得非常准确对问题精确的形式化定义是解决问题的第一步。在这一部分首先确定了问题的范围接着交代了本文的核心概念BRDF与相近概念的关系。在3.2节用图2表示了评估网络的详细架构。使用相连的三角形和梯形巧妙地表示了网络的各个组件节省了空间。在表 1 中将本文的方法与三项相关工作进行了比较突出了本文方法的特点。(In the beginning of the third section, a paragraph is used to introduce the content of this section briefly. The problem is described in 3.1 by equations. The word “formulation” is used rather accurately, as the precise formal definition of the problem is the first step in solving it. In this part, the scope of the problem is first determined, then the relationship between the core concept of the article (i.e., BRDF) and similar concepts is explained. In 3.2, Figure 2 shows the detailed structure of the evaluation network. Closely connected triangles and trapezoids are used to represent the components of the network skillfully, saving the space for typesetting. Table 1 compares the proposed method with three related works, emphasizing the feature of the proposed method.) 6在实验部分使用多种材料组合成多种分层材质进行神经网络的训练数据量充足。(In experiments, multiple kinds of materials are used to generate 12720 layered BRDFs to train the networks, which guarantees for sufficient data.)
7在结论部分回顾了主要贡献明确了当前方法的限制并对未来工作进行了展望。(In conclusion, the main contribution is recapped, the limitations of proposed method are cleared, and the future work is discussed.)
8提供了补充资料显著提高了论文的可读性。(The supplementary material is provided, which significantly improves the readability of the paper.)
9引用的参考文献较新在19篇引用文献中有14篇在近5年发表。(Relatively new references: 14 of the 19 items were published in the last 5 years.)
10全文的过渡词使用恰到好处衔接自然过渡流畅。配图美观清晰赏心悦目。(The transition words in the paper are properly used, results in natural connection and smooth transition. The figures are artistic and clear, which bring pleasant experience to the readers.)
2.4 不足Inadequacies
1摘要最后一句话中的“神经代数”neural algebra在正文中只是一笔带过有博眼球的嫌疑。(The word “neural algebra” in the last sentence of the abstract is simply described in the main body, which is a suspicion of attracting eyeballs. ) 2对于公式2没有说明Nlayering和Vlayered的含义。(As for Eq.2, the meaning of symbol Nlayering and Vlayered are not explained.) 33.2节中对于为什么要离散化输入BRDF可补充说明。(In 3.2, the necessity for the discretization of the input BRDF could be complemented.) 4没有公开代码而且没有用伪代码进行描述。(The code is not released, and pseudocodes are not used.)
