Computing the Physical Parameters of Rigid-Body Motion from Video
By K. Bhat, S. M. Seitz, Jovan Popovic. 2002
74 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Jitendra Malik
- Andrew Zisserman
- Andrea Vedaldi
- Ming-Hsuan Yang
- Dacheng Tao
- Trevor Darrell
- Xiaogang Wang
In Control and Systems Engineering
- Sergey Levine
- Jitendra Malik
- Pulkit Agrawal
- Katerina Fragkiadaki
- Joelle Pineau
- Amy Zhang
- Peter W. Battaglia
- Pieter Abbeel
Citations in the corpus
- Visual Interaction Networks: Learning a Physics Simulator from Video (2017, background): “Several efforts [4, 6, 26, 27] have fit the parameters of Newtonian mechanics equations to systems depicted in images and videos, though the dynamic equations themselves were not learned.”
- Learning Visual Predictive Models of Physics for Playing Billiards (2015, methodology): “Learning Physics from Images and Videos Works of Wu et al. (2015); Bhat et al. (2002); Brubaker et al. (2009); Mottaghi et al. (2015) propose methods for estimating the parameters of Newtonian equations from images and videos.”
- "What Happens If..." Learning to Predict the Effect of Forces in Images (2016, neutral): “[2] estimate the physical parameters of rigid objects using video data.”
- Estimating contact dynamics (2009, background): “Bhat et al [2] estimate physical properties of rigid objects in free flight but do not address the issue of contact.”
- Generating Liquid Simulations with Deformation-aware Neural Networks (2017, background): “Early examples targeted minimization problems to determine physical trajectories or contact forces [4, 7], or plausible physics for interacting objects [21, 22].”
- Block Contextual MDPs for Continual Learning (2021, background): “…Modeling and (iv) Meta RL and Multitask RL. System Identification and Adaptive Control (Zadeh, 1956; Åström & Bohlin, 1965; Swevers et al., 1997; Bhat et al., 2002; Gevers et al., 2006; Ljung, 2010; Van Overschee & De Moor, 2012; Chiuso & Pillonetto, 2019; Ajay et al., 2019; Yu et al., 2017;…”
- A Physics-Based Model Prior for Object-Oriented MDPs (2014, methodology): “, 2003), rigid-body motion (Bhat et al., 2002), and even humanoid motion (Liu et al.”