Automating Program Structure Classification

1 code implementation • 15 Jan 2021 • Will Crichton, Georgia Gabriela Sampaio, Pat Hanrahan

When students write programs, their program structure provides insight into their learning process.

Classification General Classification

Scanner: Efficient Video Analysis at Scale

1 code implementation • 18 May 2018 • Alex Poms, Will Crichton, Pat Hanrahan, Kayvon Fatahalian

The challenge is that scaling applications to operate on these datasets requires efficient systems for pixel data access and parallel processing across large numbers of machines.

Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55

1 code implementation • 17 Oct 2017 • Li Yi, Lin Shao, Manolis Savva, Haibin Huang, Yang Zhou, Qirui Wang, Benjamin Graham, Martin Engelcke, Roman Klokov, Victor Lempitsky, Yuan Gan, Pengyu Wang, Kun Liu, Fenggen Yu, Panpan Shui, Bingyang Hu, Yan Zhang, Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Minki Jeong, Jaehoon Choi, Changick Kim, Angom Geetchandra, Narasimha Murthy, Bhargava Ramu, Bharadwaj Manda, M. Ramanathan, Gautam Kumar, P Preetham, Siddharth Srivastava, Swati Bhugra, Brejesh lall, Christian Haene, Shubham Tulsiani, Jitendra Malik, Jared Lafer, Ramsey Jones, Siyuan Li, Jie Lu, Shi Jin, Jingyi Yu, Qi-Xing Huang, Evangelos Kalogerakis, Silvio Savarese, Pat Hanrahan, Thomas Funkhouser, Hao Su, Leonidas Guibas

We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.

3D Part Segmentation 3D Reconstruction +1

Submodular Trajectory Optimization for Aerial 3D Scanning

no code implementations • ICCV 2017 • Mike Roberts, Debadeepta Dey, Anh Truong, Sudipta Sinha, Shital Shah, Ashish Kapoor, Pat Hanrahan, Neel Joshi

Drones equipped with cameras are emerging as a powerful tool for large-scale aerial 3D scanning, but existing automatic flight planners do not exploit all available information about the scene, and can therefore produce inaccurate and incomplete 3D models.

Trajectory Planning

Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging

no code implementations • 22 Apr 2016 • Zachary DeVito, Michael Mara, Michael Zollhöfer, Gilbert Bernstein, Jonathan Ragan-Kelley, Christian Theobalt, Pat Hanrahan, Matthew Fisher, Matthias Nießner

Many graphics and vision problems can be expressed as non-linear least squares optimizations of objective functions over visual data, such as images and meshes.

Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks

1 code implementation • NeurIPS 2016 • Daniel Ritchie, Anna Thomas, Pat Hanrahan, Noah D. Goodman

Probabilistic inference algorithms such as Sequential Monte Carlo (SMC) provide powerful tools for constraining procedural models in computer graphics, but they require many samples to produce desirable results.

ShapeNet: An Information-Rich 3D Model Repository

13 code implementations • 9 Dec 2015 • Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qi-Xing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, Fisher Yu

We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects.

Data Visualization