#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 02026-01-14#ebook chapter #PDF about #integer-programming #optimization
on 02025-09-09fmt does #word-wrap with #dynamic-programming or similar #optimization #algorithms
on 02025-07-27“In computer science, a problem is said to have optimal substructure if an optimal solution can be constructed from optimal solutions of its subproblems.” #algorithms #optimization
on 02025-07-19#math #video #toread about #optimization in #C of Fibonacci number computation
on 02025-03-18#paper on "goSLP", which uses linear #optimization (ILP) for #SIMD #vectorization, achieving significant #performance gains on floating-point benchmarks like SPEC2017fp. “Using an integer linear programming (ILP) solver, goSLP searches the entire space of statement packing opportunities for a whole function at a time, while limiting total compilation time to a few minutes. Furthermore, goSLP optimally solves the vector permutation selection problem using dynamic programming.” #compilers
on 02024-12-12the main #documentation on how to use #Z3, including for #optimization #SAT #SMT
on 02024-06-23“Specification gaming examples in #AI - master list” on #optimization systems finding ways to cheat on their metrics Goodhart's-Law-style; I think this is where the arXiv paper came from #humor
on 02023-02-16“The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities” on #optimization systems finding ways to cheat on their metrics Goodhart's-Law-style, like Karl Sims’s creatures that fell over, etc. #AI
on 02023-01-15Justin Meiners’s advocacy for #optimization, using the Nelder-Mead #algorithm in #JS and using drawn figure recognition as the example problem
on 02022-05-21“Mitsuba 2: A Retargetable Forward and Inverse Renderer” is a #differentiable #3D ray tracer with a #GPGPU backend used for, among other things, #rendering #caustics and #optimization for analysis-by-synthesis computer vision
on 02021-02-09“DEODR (for Discontinuity-Edge-Overdraw based Differentiable Renderer) is a #differentiable #3D mesh renderer written in C with Python and Matlab bindings.” for “efficient analysis-by-synthesis computer vision” from 02008! #graphics #optimization #software #rendering
on 02021-02-09#software of #SDFDiff #differentiable #signed-distance-fields #3D #optimization #graphics #rendering. Proprietary by default tho
on 02021-02-09#video of "SDFDiff" #Differentiable #Rendering of #Signed-Distance-Fields for #3D Shape #Optimization (CVPR2020 Oral), using a multiresolution strategy. #graphics
on 02021-02-09#Video of the #DIST #differentiable #graphics #rendering software #3D scanning a couple of chairs with #optimization
on 02021-02-09#3D reconstruction with #differentiable #optimization using sphere tracing of a #signed-distance-field with the "DIST" #graphics #rendering software
on 02021-02-09discussion of (MIT) Li et al.'s #graphics rasterizer that’s differentiable (because it uses a sigmoid where normal rasterizers would use a step function) for #optimization
on 02021-01-15The SciPy #Python #library comes with a whole passel of #optimization #algorithms.
on 02017-07-13using #DSP #comb-filters to remove 50Hz mains hum and all its harmonics, with and without removing DC, optimizing the filter coefficients using #downhill-simplex #optimization
on 02017-05-19one of various derivative-free #metaheuristics for #optimization, shrinking and flipping a simplex (an N-dimensional tetrahedron) toward the local minimum, aka "downhill simplex"
on 02017-05-19#paper on escaping saddle points efficiently in #gradient-descent #optimization (with nearly-dimension-free performance)
on 02017-03-30A #PDF chapter on finite-domain constraint #logic programming in #Prolog, including #optimization problems, with examples like cryptarithms and Hamiltonian cycle optimization. #toread
on 02016-10-11Generating faces with deconvolution #neural-networks, supporting fairly realistic face interpolation. Lots of fun pictures from different kinds of #optimization #algorithms.
on 02016-10-03the earlier 2013 research on #kinematics based #generative design of animatronic characters by #optimization of #linkages by #Disney
on 02016-06-27#video on #kinematics based #generative design of animatronic characters by #optimization of #linkages, research from #Disney
on 02016-06-272014 paper on #kinematics based #generative design of animatronic characters by #optimization of #linkages, research from #Disney
on 02016-06-27#ebook #pdf textbook used in the mathematical #optimization course taught at MIT. It focuses almost entirely on linear programming, plus a chapter each on dynamic programming, nonlinear programming (quadratic objective functions and whatnot), and network programming. Not nearly as heamy as I was hoping.
on 02016-06-06“An overview of #gradient-descent #optimization #algorithms” “This blog post aims at providing you with intuitions towards the behaviour of different algorithms for optimizing gradient descent that will help you put them to use”
on 02016-05-04#pdf #introduction to local-search #optimization; 41 #slides. “If you want to pick one local search algorithm, learn hill-climbing with random restarts!” Defines “hill-climbing” in a way that excludes continuous state spaces, and always chooses the best neighbor, rather than picking any better neighbor.
on 02016-01-13A repository of papers about #optimization.
on 02016-01-13Quick #pdf #introduction to #gradient-descent #optimization #algorithms.
on 02016-01-13The general statement of #optimization.
on 02016-01-13“local search” is the family of #optimization #algorithms that includes hill climbing, gradient descent, simulated annealing, and tabu search.
on 02016-01-13more theorems about #complexity of #optimization
on 02016-01-13interesting, the same David Wolpert who just extended Landauer’s bound to arbitrary computation proved an interesting theorem about the computational #complexity of general #optimization problems
on 02016-01-13A #paper about #trading: “An Optimization-Based Framework for Automated Market-Making (2011)”, reducing market-making to convex #optimization, and by relaxing the convex hull you can get the #algorithms to be computationally tractable without sucking too badly.
on 02015-08-13