A probabilistic model for component-based shape synthesis

By Evangelos Kalogerakis, Siddhartha Chaudhuri, Daphne Koller, Vladlen Koltun. 2012

407 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Leonidas J. Guibas
2. Matthias Nießner
3. Thomas A. Funkhouser
4. Andreas Geiger
5. Charles Ruizhongtai Qi
6. Hao Su
7. Niloy J. Mitra
8. Jitendra Malik

In Computer Graphics and Computer-Aided Design

1. Leonidas J. Guibas
2. Niloy J. Mitra
3. Thomas A. Funkhouser
4. Hao Su
5. Jiajun Wu
6. Josh Tenenbaum
7. P. Hanrahan
8. Hao Zhang

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Jitendra Malik
3. Trevor Darrell
4. Ming-Hsuan Yang
5. Dacheng Tao
6. Xiaogang Wang
7. Andrea Vedaldi
8. Andrew Zisserman

Citations in the corpus

• Learning Descriptor Networks for 3D Shape Synthesis and Analysis (2018, background): “Researchers in the fields of graphics and vision have studied the 3D object synthesis problems [2, 3, 9].”
• ShapeNet: An Information-Rich 3D Model Repository (2015, methodology): “Mirroring this pattern, recent work in computer graphics has also applied similar approaches to specific problems in the synthesis of new shape variations [10] and new arrangements of shapes [6].”
• Category-specific object reconstruction from a single image (2014, background): “[22] and involve complicated formulations that are difficult to optimize.”
• Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks (2017, background): “Different approaches for modeling 3D shapes fall into three key categories: parts-based [8, 10, 1, 26], voxel-based [19, 27, 6, 28], and view based methods [18, 23].”
• SurfNet: Generating 3D Shape Surfaces Using Deep Residual Networks (2017, background): “Recent approaches for assembly-based 3D shape creation from components use probabilistic models [5, 15], or deep-learned models [13].”
• Garment modeling with a depth camera (2015, background): “We follow [Chaudhuri et al. 2011; Kalogerakis et al. 2012] to build a Bayesian network that captures the contextual prior from the labeled image database.”
• PCN: Point Completion Network (2018, background): “Some [15, 24, 32, 34, 43] retrieve the complete shape directly while some [17, 19, 28, 45, 52] retrieve object parts and then assemble them to obtain the complete shape.”
• PolyGen: An Autoregressive Generative Model of 3D Meshes (2020, methodology): “Existing approaches to 3D object synthesis rely on the recombination and deformation of template models (Kalogerakis et al., 2012; Chaudhuri et al., 2011), or a parametric shape family (Smelik et al.”
• Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images Using a View-Based Representation (2020, background): “tensively in the computer vision and graphics communities (Saxena et al. 2009; Chaudhuri et al. 2011; Kalogerakis et al. 2012; Chang et al. 2015; Rezende et al. 2016; Soltani et al. 2017; Kulkarni et al. 2015; Tulsiani et al. 2016; Huang et al. 2019; Jiang et al. 2019).”
• Interactive 3D Modeling with a Generative Adversarial Network (2017, methodology): “ons between surfaces. For example, [17], [1], and others describe methods for interpolation between surfaces with consistent parameterizations. More recently, probabilistic models of part hierarchies [16], [14] and grammars of shape features [8] have been learned from collections and used to assist ”