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Topic

Graphics

About: Graphics is a research topic. Over the lifetime, 17394 publications have been published within this topic receiving 411468 citations. The topic is also known as: graphic.


Papers
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Journal ArticleDOI
TL;DR: JaxoDraw is a Feynman graph plotting tool written in Java that has a complete graphical user interface that allows all actions to be carried out via mouse click-and-drag operations in a WYSIWYG fashion.

841 citations

Journal ArticleDOI
TL;DR: In this paper, an integrated view of learning from verbal and pictorial representations is presented, where learning from these representations is considered as a task oriented process of constructing multiple mental representations, including information selection and information organisation, parsing of symbol structures, mapping of analog structures as well as model construction and model inspection.

838 citations

Journal ArticleDOI
TL;DR: RIBBONS 2.0 as mentioned in this paper allows real-time viewing of solid shaded ribbon models of macromolecules, including spheres, cylinders, dots, polygons and text.
Abstract: The program RIBBONS 2.0 allows real-time viewing of solid shaded ribbon models of macromolecules. The primary features of the software are the ability to create a wide variety of styles of ribbon drawings interactively and to toggle between various coloring schemes chosen to reflect assorted geometrical and biochemical properties. Spheres, cylinders, dots, polygons and text are also supported. The auxiliary programs included make RIBBONS 2.0 a powerful tool for visual structural analysis as well as for presentation graphics. The program is currently available only for the Silicon Graphics 4D series of workstations. A port to the Evans & Sutherland ESV workstation employing PEX is under development.

823 citations

Posted Content
TL;DR: An approach for selecting problem-specific Fourier features that greatly improves the performance of MLPs for low-dimensional regression tasks relevant to the computer vision and graphics communities is suggested.
Abstract: We show that passing input points through a simple Fourier feature mapping enables a multilayer perceptron (MLP) to learn high-frequency functions in low-dimensional problem domains These results shed light on recent advances in computer vision and graphics that achieve state-of-the-art results by using MLPs to represent complex 3D objects and scenes Using tools from the neural tangent kernel (NTK) literature, we show that a standard MLP fails to learn high frequencies both in theory and in practice To overcome this spectral bias, we use a Fourier feature mapping to transform the effective NTK into a stationary kernel with a tunable bandwidth We suggest an approach for selecting problem-specific Fourier features that greatly improves the performance of MLPs for low-dimensional regression tasks relevant to the computer vision and graphics communities

787 citations

Journal ArticleDOI
TL;DR: A versatile new input encoding that permits the use of a smaller network without sacrificing quality, thus significantly reducing the number of floating point and memory access operations is introduced, enabling training of high-quality neural graphics primitives in a matter of seconds, and rendering in tens of milliseconds at a resolution of 1920×1080.
Abstract: Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate. We reduce this cost with a versatile new input encoding that permits the use of a smaller network without sacrificing quality, thus significantly reducing the number of floating point and memory access operations: a small neural network is augmented by a multiresolution hash table of trainable feature vectors whose values are optimized through stochastic gradient descent. The multiresolution structure allows the network to disambiguate hash collisions, making for a simple architecture that is trivial to parallelize on modern GPUs. We leverage this parallelism by implementing the whole system using fully-fused CUDA kernels with a focus on minimizing wasted bandwidth and compute operations. We achieve a combined speedup of several orders of magnitude, enabling training of high-quality neural graphics primitives in a matter of seconds, and rendering in tens of milliseconds at a resolution of 1920×1080.

782 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023469
20221,141
2021208
2020349
2019355
2018399