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ACM Transactions on Graphics
H-index 85

ACM Transactions on Graphics

0730-0301

Published by: ACM

https://dl.acm.org/journal/tog

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Computer Science 24 326 915 85

Additional Metrics

Number of Best Scientists*: 379
Documents by Best Scientists*: 987
Top 100 Ranked Scientists*: 16
SCIMAGO H-index: 264
SCIMAGO SJR: 2.965
Impact Factor: 9.5

Overview

Top Research Topics at ACM Transactions on Graphics?

ACM Transactions on Graphics is organized to address concerns in the fields of Artificial intelligence, Computer vision, Algorithm, Rendering (computer graphics) and Computer graphics (images). The journal holds forums on Artificial intelligence that merges themes from other disciplines such as Animation and Pattern recognition. The studies tackled, which mainly focus on Computer vision, apply to Computer graphics as well.

Polygon mesh, Mathematical optimization and Surface (mathematics) are some topics wherein Algorithm research discussed in ACM Transactions on Graphics have an impact. Polygon mesh and Topology are closely related fields of research discussed in it. The journal aims to address concerns in Rendering (computer graphics), specifically in the areas of Global illumination and Path tracing.

Aside from research in Computer graphics (images), ACM Transactions on Graphics also discusses Session (computer science) studies.

  • Artificial intelligence (30.92%)
  • Computer vision (24.51%)
  • Algorithm (17.67%)

What are the most cited papers published in the journal?

  • Three-dimensional alpha shapes (1771 citations)
  • Synthesis and evaluation of linear motion transitions (1549 citations)
  • Shape distributions (1459 citations)

Research areas of the most cited articles at ACM Transactions on Graphics:

The journal papers primarily tackle Artificial intelligence, Computer vision, Algorithm, Rendering (computer graphics) and Computer graphics (images). The journal papers focus on Artificial intelligence but the discussions also offer insight into other areas such as Animation and Pattern recognition. The most cited papers investigate Computer vision research which frequently intersects with Computer graphics.

What topics the last edition of the journal is best known for?

  • Artificial intelligence
  • Optics
  • Geometry

The previous edition focused in particular on these issues:

Object (computer science), Topology (chemistry), Rope, Topology and Tree (data structure) are the subjects of interest in ACM Transactions on Graphics. ACM Transactions on Graphics explores topics in Tree (data structure) which can be helpful for research in disciplines like Artificial neural network, Artificial intelligence and Pattern recognition.

The most cited articles from the last journal are:

  • SGN: Sparse Gauss-Newton for Accelerated Sensitivity Analysis (0 citations)
  • Computational Object-Wrapping Rope Nets (0 citations)
  • ICTree: Automatic Perceptual Metrics for Tree Models (0 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

The top authors publishing in ACM Transactions on Graphics (based on the number of publications) are:

  • Daniel Cohen-Or (47 papers) absent at the last edition,
  • Ravi Ramamoorthi (36 papers) absent at the last edition,
  • Wojciech Matusik (28 papers) absent at the last edition,
  • Frédo Durand (28 papers) absent at the last edition,
  • Hans-Peter Seidel (27 papers) absent at the last edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Only papers with recognized affiliations are considered

The top affiliations publishing in ACM Transactions on Graphics (based on the number of publications) are:

  • Adobe Systems (111 papers) absent at the last edition,
  • Massachusetts Institute of Technology (101 papers) absent at the last edition,
  • Stanford University (93 papers) absent at the last edition,
  • Microsoft (73 papers) absent at the last edition,
  • Carnegie Mellon University (62 papers) absent at the last edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2022 edition, 33.33% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 50.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 0.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 0.00% of all publications and 50.00% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Potential Opportunities for Future Research and Development

While the ACM Transactions on Graphics presents detailed and informative findings on various graphics research topics, the journal provides ample opportunities for future research and developments. As research interests continue to evolve and expand, several pertinent areas could be further explored. Recent advancements in Artificial Intelligence and Computer Vision have paved the way for the development and implementation of more sophisticated algorithms. It begs the question: What are the future applications of these evolving technologies? Furthermore, readers might be interested in how they could contribute to the field. For instance, aspiring researchers and professionals might wonder about the qualifications they should possess or steps they must take to becoming involved in research as robust as those published in ACM Transactions on Graphics. If one's area of interest lies in children's education, for example, learning how to become an elementary teacher in Indiana can provide a starting point. It's essential to consistently present new challenges, theories, and hypotheses to probe in future editions of the journal. This helps in promoting continual research and innovation, causing the field to progress and remain relevant. By presenting these areas of particular interest, ACM Transactions on Graphics can continue to inspire and guide its readership, fostering a community of progressive, forward-thinking researchers and professionals.

Top Publications

  • 3D Gaussian Splatting for Real-Time Radiance Field Rendering

    Unknown

    (2023)
    3449 Citations
  • Instant neural graphics primitives with a multiresolution hash encoding

    (2022)
    3237 Citations
  • Designing an encoder for StyleGAN image manipulation

    (2021)
    722 Citations
  • HyperNeRF

    Unknown

    (2021)
    699 Citations
  • Designing an encoder for StyleGAN image manipulation

    Omer Tov;Yuval Alaluf;Yotam Nitzan;Or Patashnik

    (2021)
    679 Citations
  • StyleGAN-NADA

    Unknown

    (2021)
    622 Citations
  • StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows

    Rameen Abdal;Peihao Zhu;Niloy J. Mitra;Peter Wonka

    (2021)
    504 Citations
  • Pivotal Tuning for Latent-based Editing of Real Images

    (2021)
    489 Citations
  • Learning an animatable detailed 3D face model from in-the-wild images

    Yao Feng;Haiwen Feng;Michael J. Black;Timo Bolkart

    (2021)
    417 Citations
  • Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models

    (2023)
    403 Citations

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Best Scientists Contributing to This Journal