#Scott-Alexander explains his #atheism in terms of #Bayesian #epistemology
on 02026-08-19#SIGGRAPH ’24 “thesis fast forward” half-hour #video covering 9 different #graphics dissertations.
Ruben Wiersma talks about applying 2-D neural networks on 3-D meshes in a few different ways, including three that you can pip install: deltaconv pcdiff gravomg.
Chenxi Liu talks about her #algorithms for analyzing vector sketches that artists can use to communicate visual ideas, so far apparently used only for sketch simplification and flood fill.
Rohan Sawhney talks about Monte Carlo geometry processing, specifically to solve PDEs on complex geometry without the volumetric meshing #FEM needs, using “Muller 1956”’s “Walk on Spheres” to interpolate boundary conditions into the interior of a region, using something that sounds similar to an SDF, though he doesn’t call it that; like a “ray tracer” for physics (versus FEM’s “triangle rendering”).
Silvia Sellán talks about faster computation of swept volumes, as well as some other 3-D problems like surface reconstruction; I don’t really understand the common thread between these, though she says it’s “uncertainty quantification”.
Xilong Zhou talks about how to acquire “materials” such as bricks, marble, or ceramic tile, from photos, to use their BRDFs or SVBRDFs in rendering, with some kind of #Bayesian approach.
“Hi, my name is Dr. Zachary Ferguson” talks about a new numerical method for #simulation that don’t experience numerical instability (explosion) when simulated surfaces come into contact and the time step isn’t short enough; it’s called "incremental potential contact", with a new smooth barrier function (with a singularity!) enabling Newton’s method with line search to solve the contact correctly, using continuous collison detection (“CCD”). He claims that his work has “sparked a revolution in physical simulation”, although if that’s true, I don’t know why he feels the need to introduce himself as "Dr. Zachary Ferguson", as if he were used to being ignored and dismissed.
Pascal Guehl [geɪł] talks about texture/material synthesis, what he calls “semi-procedural”, combining the advantages of “by-example” texture synthesis (trying to make things look like a photo) with procedural (adding up noise functions and frequency components). It works by finding the “closest procedural model” to a given example image. Looks really cool.
S. Mazdak “Maz” Abdulnaga talks about volumetric mapping for medical imaging, in particular by minimizing the distortion energy of a volumetric map (ℝ³ → ℝ³) between two target volumes; the energy is defined in a symmetric way, so it doesn’t change when you swap the volumes. For example, you can use this to analyze placental health during pregnancy by mapping 3-D MRI scans taken over time to one another. Other applications include improving texture transfer for surfaces.
Yiwei Hu talks about “efficient material authoring by inverse material modeling”, tackling the same problem as Pascal Guehl in more or less the same way, but using CNN #neural-networks and gradient-based #optimization; his approach seems to handle some cases like checkerboard patterns better than Guehl’s.
on 02026-01-14Jaynes's paper about how applying #Jeffreys style #Bayesian probability to #physics clears up a lot of mysteries
on 02023-06-27Cox’s theorem is the one that derives #Bayesian probability from logic, given “divisibility and comparability”, “common sense”, and “consistency”, as presented by Edwin Thompson Jaynes in “Probability Theory: The Logic of Science”.
on 02021-05-29#Probabilistic-Programming and #Bayesian methods for Hackers in pure #Python. #ebook
on 02015-08-19#bayesian #statistics #philosophy #toread
on 02015-08-05