Context-based search for 3D models
By Matthew Fisher, P. Hanrahan. 2010
124 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
- Jiajun Wu
- Josh Tenenbaum
- Zhoutong Zhang
- William T. Freeman
- Chengkai Zhang
- Xiuming Zhang
- Xingyuan Sun
- Jitendra Malik
In Computer Graphics and Computer-Aided Design
- Jiajun Wu
- Antonio Torralba
- Niloy J. Mitra
- Josh Tenenbaum
- William T. Freeman
- Xingyuan Sun
- Tianfan Xue
- Martial Hebert
In Computer Vision and Pattern Recognition
- Jitendra Malik
- Luc Van Gool
- Andrea Vedaldi
- Abhinav Kumar Gupta
- Ming-Hsuan Yang
- Li Fei-Fei
- Dacheng Tao
- Trevor Darrell
Citations in the corpus
- Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling (2018, background): “Another related research direction is retrieving similar 3D shapes given a single image, instead of reconstructing the object’s actual geometry [1, 15, 19, 48].”
- Hallucinating Humans for Learning Robotic Placement of Objects (2012, unknown): “[30, 31] designed a context-based search engine using geometric cues and spatial relationships to find the proper object for a given scene.”
- Creating consistent scene graphs using a probabilistic grammar (2014, methodology): “2013], context-based object retrieval [Fisher and Hanrahan 2010], scene retrieval [Fisher et al.”
- SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation (2019, background): “Early work in 3D scene modeling employed hand-engineered kernels and graph walks to retrieve objects from a database of 3D models that are compatible with the surrounding context of an input scene [3, 5].”
- Learning to place new objects in a scene (2012, background): “Fisher and Hanrahan (2010) and Fisher et al. (2011) designed a context-based search engine using geometric cues and spatial relationships to find the proper object for a given scene.”
- FPM: Fine Pose Parts-Based Model with 3D CAD Models (2014, background): “Various recent works have tackled the problem of object-level pose estimation [37,21,11].”
- 3DNN: 3D Nearest Neighbor (2014, background): “The graphics community has begun harvesting data from online repositories such as 3D Warehouse in an effort to better understand and model how objects are typically arranged in homes (Fisher and Hanrahan 2010; Fisher et al. 2011).”