Daniel Ritchie
Daniel Ritchie
Verified email at brown.edu - Homepage
TitleCited byYear
Example-based synthesis of 3D object arrangements
M Fisher, D Ritchie, M Savva, T Funkhouser, P Hanrahan
ACM Transactions on Graphics (TOG) 31 (6), 135, 2012
Interactive simulation of surgical needle insertion and steering
N Chentanez, R Alterovitz, D Ritchie, L Cho, KK Hauser, K Goldberg, ...
ACM Transactions on Graphics (TOG) 28 (3), 88, 2009
Dynamic local remeshing for elastoplastic simulation
M Wicke, D Ritchie, BM Klingner, S Burke, JR Shewchuk, JF O'Brien
ACM Transactions on graphics (TOG) 29 (4), 49, 2010
Probabilistic color-by-numbers: Suggesting pattern colorizations using factor graphs
S Lin, D Ritchie, M Fisher, P Hanrahan
ACM Transactions on Graphics (TOG) 32 (4), 37, 2013
Controlling procedural modeling programs with stochastically-ordered sequential monte carlo
D Ritchie, B Mildenhall, ND Goodman, P Hanrahan
ACM Transactions on Graphics (TOG) 34 (4), 105, 2015
Scancomplete: Large-scale scene completion and semantic segmentation for 3d scans
A Dai, D Ritchie, M Bokeloh, S Reed, J Sturm, M Nießner
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
Learning to infer graphics programs from hand-drawn images
K Ellis, D Ritchie, A Solar-Lezama, J Tenenbaum
Advances in neural information processing systems, 6059-6068, 2018
Deep amortized inference for probabilistic programs
D Ritchie, P Horsfall, ND Goodman
arXiv preprint arXiv:1610.05735, 2016
d. tour: Style-based exploration of design example galleries
D Ritchie, AA Kejriwal, SR Klemmer
Proceedings of the 24th annual ACM symposium on User interface software and …, 2011
Deep convolutional priors for indoor scene synthesis
K Wang, M Savva, AX Chang, D Ritchie
ACM Transactions on Graphics (TOG) 37 (4), 70, 2018
Neurally-guided procedural models: Amortized inference for procedural graphics programs using neural networks
D Ritchie, A Thomas, P Hanrahan, N Goodman
Advances in neural information processing systems, 622-630, 2016
C3: Lightweight incrementalized MCMC for probabilistic programs using continuations and callsite caching
D Ritchie, A Stuhlmüller, N Goodman
Artificial Intelligence and Statistics, 28-37, 2016
First-class runtime generation of high-performance types using exotypes
Z DeVito, D Ritchie, M Fisher, A Aiken, P Hanrahan
ACM SIGPLAN Notices 49 (6), 77-88, 2014
Generating Design Suggestions under Tight Constraints with Gradient‐based Probabilistic Programming
D Ritchie, S Lin, ND Goodman, P Hanrahan
Computer Graphics Forum 34 (2), 515-526, 2015
Improving shape deformation in unsupervised image-to-image translation
A Gokaslan, V Ramanujan, D Ritchie, K In Kim, J Tompkin
Proceedings of the European Conference on Computer Vision (ECCV), 649-665, 2018
Neurally-guided procedural models: learning to guide procedural models with deep neural networks
D Ritchie, A Thomas, P Hanrahan, ND Goodman
arXiv preprint arXiv:1603.06143, 2016
Fast and flexible indoor scene synthesis via deep convolutional generative models
D Ritchie, K Wang, Y Lin
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
Learning to describe scenes with programs
Y Liu, Z Wu, D Ritchie, WT Freeman, JB Tenenbaum, J Wu
Example‐based Authoring of Procedural Modeling Programs with Structural and Continuous Variability
D Ritchie, S Jobalia, A Thomas
Computer Graphics Forum 37 (2), 401-413, 2018
Matt’s webcorner
M Fisher
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