An efficient representation for irradiance environment maps

By Ravi Ramamoorthi, P. Hanrahan. International Conference on Computer Graphics and Interactive Techniques, 2001

976 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/88d527a4fa5c1b48dc72159932c48dc155c2d608

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In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

Clustered principal components for precomputed radiance transfer (2003, background): “The spherical harmonic (SH) basis provides a compact, alias-avoiding representation for functions of radiance over a sphere or hemisphere [Cabral et al. 1987][Sillion et al. 1991][Westin et al. 1992][Ramamoorthi and Hanrahan 2001].”
Exposing Digital Forgeries in Complex Lighting Environments (2007, methodology): “The spherical harmonic coefficients are computed by integrating the lighting environment function against the corresponding spherical harmonic basis function [20]”
Adversarial Geometry and Lighting using a Differentiable Renderer (2018, methodology): “For instance, rendering of diffuse materials can be approximated with only 1% pixel intensity error by the first 2 orders of spherical harmonics [34].”
Intrinsic Scene Properties from a Single RGB-D Image (2013, background): “Where R is a log-reflectance image, Z is a depth-map, L is a spherical-harmonic model of illumination [25], and S(Z,L) is a “rendering engine” which produces a logshading image given Z and L.”
Precomputed Radiance Transfer for Real-Time Rendering in Dynamic, Low-Frequency Lighting Environments (2002, methodology): “The convolution property provides a fast way to convolve an environment map with a hemispherical cosine kernel, defined as ( ) max( ,0) , to get an irradiance map [ 34 ], for which the”
Learning an animatable detailed 3D face model from in-the-wild images (2020, methodology): “Illumination model: For face reconstruction, the most frequently-employed illumination model is based on Spherical Harmonics (SH) [Ramamoorthi and Hanrahan 2001].”
3D Morphable Face Models—Past, Present, and Future (2019, neutral): “A better approximation of complex natural illumination is provided by the spherical harmonic illumination model [Ramamoorthi and Hanrahan 2001].”
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer (2019, methodology): “We also support 3 different local illumination models: Phong [26], Lambertian [16] and Spherical Harmonics [27], where the lighting effect is related to normal, light and eye directions.”
Ieee Transactions on Pattern Analysis and Machine Intelligence Shape, Illumination, and Reflectance from Shading (positive): “The SIRFS problem formulation is: maximize R,Z,L P (R)P (Z)P (L) subject to I = R + S(Z, L) (1) Where R is a log-reflectance image, Z is a depth-map, and L is a spherical-harmonic model of illumination [45].”
Tex2Shape: Detailed Full Human Body Geometry From a Single Image (2019, methodology): “For rendering SH are used to describe the directions from where light is shining into the scene [41].”
Acquiring linear subspaces for face recognition under variable lighting (2005, methodology): “In this section, we briefly summarize the recent work presented in [2], [ 14 ], [15], [19].”
Accurate 3D Face Reconstruction With Weakly-Supervised Learning: From Single Image to Image Set (2019, methodology): “We assume a Lambertian surface for face and approximate the scene illumination with Spherical Harmonics (SH) [35, 36].”
Learning to reconstruct shape and spatially-varying reflectance from a single image (2018, methodology): “We follow the method of [Ramamoorthi and Hanrahan 2001] to render an image of the object, ˜ I e , using the estimated spherical harmonics illumination.”
Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF From a Single Image (2019, background): “Spherical harmonics are a compact lighting representation that have been used in recent works [24, 33], but do not efficiently recover high frequency lighting necessary to handle specular effects [42, 7].”
A signal-processing framework for inverse rendering (2001, methodology): “We have already shown [28] how to use the 9 term irradiance approximation for efficient forward rendering of diffuse objects with environment maps, and we believe there are many further applications.”