SceneGrok

By P. Hanrahan. 2014

85 citations recorded by the source corpus.

Subfields: Aerospace Engineering, 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 Aerospace Engineering

1. Matthias Nießner
2. Angel X. Chang
3. Manolis Savva
4. Song-Chun Zhu
5. Kristen Grauman
6. Michael J. Black
7. Dimitrios Tzionas
8. Matthew Fisher

In Computer Vision and Pattern Recognition

1. Jitendra Malik
2. Luc Van Gool
3. Kristen Grauman
4. Trevor Darrell
5. Li Fei-Fei
6. Abhinav Kumar Gupta
7. Cordelia Schmid
8. Michael J. Black

Citations in the corpus

• Learning Affordance Landscapes for Interaction Exploration in 3D Environments (2020, background): “In computer vision, visual affordances are explored in various forms: predicting where to grasp an object from images and video [31, 32, 64, 38, 19, 62, 15, 5], inferring how people might use a space [48, 39] or tool [65], and priors for human body poses [26, 52, 58, 17].”
• PiGraphs (2016, methodology): “Recent work uses RGB-D interaction observations to predict the likelihood of actions in 3D scenes [Savva et al. 2014].”
• Holistic++ Scene Understanding: Single-View 3D Holistic Scene Parsing and Human Pose Estimation With Human-Object Interaction and Physical Commonsense (2019, background): “In robotics and computer vision, prior work has exploited human-object relations in event, object, and scene modeling, but most work focuses on human-object relation detection in images [5, 30, 25, 21], probabilistic modeling from multiple data sources [45, 33, 13], and snapshots generation or scene”
• Scene Understanding by Reasoning Stability and Safety (2015, background): “…many semantic labeling methods for 3D point clouds play an important role in robotics: Koppula et al. (2011); Anand et al. (2012); Wu et al. (2014), etc; in graphics: e.g., Nan et al. (2012); Shao et al. (2012) (2014); Savva et al. (2014), etc; in 3D shape recognition: Karpathy et al. (2013), etc.”
• GRAB: A Dataset of Whole-Body Human Grasping of Objects (2020, methodology): “[61] use RGB-D to capture 3D body skeletons of 5 subjects interacting in 30 3D scenes, to learn to synthesize interactions [61], affordance detection [60], or to reconstruct interaction from videos [42].”
• Resolving 3D Human Pose Ambiguities With 3D Scene Constraints (2019, methodology): “Finally, [61] employs similar observations to predict action maps in a 3D scene.”
• Grounded Human-Object Interaction Hotspots From Video (2018, background): “In scene understanding, they help decide where in a scene a particular action can be performed [46, 18, 59, 9], learn scene geometry [21, 15], or understand social situations [5].”