Search Results for author: Nathan Carr

Found 7 papers, 2 papers with code

FAME: 3D Shape Generation via Functionality-Aware Model Evolution

1 code implementation9 May 2020 Yanran Guan, Han Liu, Kun Liu, Kangxue Yin, Ruizhen Hu, Oliver van Kaick, Yan Zhang, Ersin Yumer, Nathan Carr, Radomir Mech, Hao Zhang

Our tool supports constrained modeling, allowing users to restrict or steer the model evolution with functionality labels.


Learning Generative Models of Shape Handles

no code implementations CVPR 2020 Matheus Gadelha, Giorgio Gori, Duygu Ceylan, Radomir Mech, Nathan Carr, Tamy Boubekeur, Rui Wang, Subhransu Maji

We present a generative model to synthesize 3D shapes as sets of handles -- lightweight proxies that approximate the original 3D shape -- for applications in interactive editing, shape parsing, and building compact 3D representations.

DiffTaichi: Differentiable Programming for Physical Simulation

2 code implementations ICLR 2020 Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, Frédo Durand

We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators.

Physical Simulations

Fast Spatially-Varying Indoor Lighting Estimation

no code implementations CVPR 2019 Mathieu Garon, Kalyan Sunkavalli, Sunil Hadap, Nathan Carr, Jean-François Lalonde

We propose a real-time method to estimate spatiallyvarying indoor lighting from a single RGB image.

SeeThrough: Finding Chairs in Heavily Occluded Indoor Scene Images

no code implementations28 Oct 2017 Moos Hueting, Pradyumna Reddy, Vladimir Kim, Ersin Yumer, Nathan Carr, Niloy Mitra

Discovering 3D arrangements of objects from single indoor images is important given its many applications including interior design, content creation, etc.

Object Detection

PatchMatch-Based Automatic Lattice Detection for Near-Regular Textures

no code implementations ICCV 2015 Siying Liu, Tian-Tsong Ng, Kalyan Sunkavalli, Minh N. Do, Eli Shechtman, Nathan Carr

In this work, we investigate the problem of automatically inferring the lattice structure of near-regular textures (NRT) in real-world images.

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