An Energy Minimization Approach to 3D Non-Rigid Deformable Surface Estimation Using RGBD Data
August 02, 2017 Β· Declared Dead Β· π 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Bryan Willimon, Steven Hickson, Ian Walker, Stan Birchfield
arXiv ID
1708.00940
Category
cs.CV: Computer Vision
Cross-listed
cs.RO
Citations
19
Venue
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
Last Checked
4 months ago
Abstract
We propose an algorithm that uses energy mini- mization to estimate the current configuration of a non-rigid object. Our approach utilizes an RGBD image to calculate corresponding SURF features, depth, and boundary informa- tion. We do not use predetermined features, thus enabling our system to operate on unmodified objects. Our approach relies on a 3D nonlinear energy minimization framework to solve for the configuration using a semi-implicit scheme. Results show various scenarios of dynamic posters and shirts in different configurations to illustrate the performance of the method. In particular, we show that our method is able to estimate the configuration of a textureless nonrigid object with no correspondences available.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Computer Vision
π
π
Old Age
π
π
Old Age
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
π
π
Old Age
SSD: Single Shot MultiBox Detector
π
π
Old Age
Squeeze-and-Excitation Networks
π
π
Old Age
Fast R-CNN
π
π
Old Age
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted