Fundamentals of computer graphics

By Peter Shirley. 2018

496 citations recorded by the source corpus.

The broad acceptance and use of this book as one of the leading introductory computer graphics texts has enabled the original author and coauthors to update the chapters and contribute new material involving the most knowledgeable experts in different areas while maintaining a unified approach. This edition of "Fundamentals of Computer Graphics" adds four new contributed chapters and contains substantial reorganizations and improvements to the core material. The new chapters add coverage of implicit modeling and of two important graphics applications: games and information visualization. The fourth new contributed chapter is a major upgrade to the material on color science. As with the chapters added in the second edition, we have chosen the contributors both for their expertise and for their clear way of expressing ideas.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

Strike (With) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects (2018, methodology): “, a list of triangular faces, each defined by three vertices [27].”
Neural 3D Mesh Renderer (2017, methodology): “This transformation is represented by a combination of differentiable transformations [16].”
Capturing tree crown formation through implicit surface reconstruction using airborne lidar data (2009, methodology): “In general, the subdivision surface approach (Bloomenthal et al.,1997) and the Non-Uniform Rational B-Spline (NURB) surface ( Shirley, 2005 ) were utilized.”
Rendering Physically Correct Raindrops on Windshields for Robustness Verification of Camera-based Object Recognition (2018, background): “For more information on refraction see established literature [28].”
BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images (2020, methodology): “The computer graphics pipeline implements perspective projection using a projective transform that converts objects from world coordinates (our scene space) to camera coordinates [34].”
BOP Challenge 2020 on 6D Object Localization (2020, background): “ed on rasterization, e.g. OpenGL, can approximate the complex eects in an ad hoc way through custom shaders, but the approximations cause physically incorrect artifacts that are dicult to eliminate [38]. BlenderProc4BOP implements a PBR synthesis approach similar to [29]. However, to improve eciency”
Learning an appearance-based gaze estimator from one million synthesised images (2016, methodology): “We therefore developed physically correct refractive effects using a fragment shader – a GPU program that processes each pixel in a graphics pipeline [Shirley et al. 2009].”
Efficient Segmentation: Learning Downsampling Near Semantic Boundaries (2019, methodology): “We use Scipy [2] to interpolate the unstructured multidimensional data, which employs Delaunay [12] triangulation and barycentric interpolation within triangles [48].”