10 citations · 22 across the 7 of their papers we have counts for
5 papers · 1 filter
Self-supervised Neural Articulated Shape and Appearance Models
Fangyin Wei, Rohan Chabra, Lingni Ma +6
Learning geometry, motion, and appearance priors of object classes is important for the solution of a large variety of computer vision problems. While the majority of approaches ha…
Learning to Infer Semantic Parameters for 3D Shape Editing
Fangyin Wei, Elena Sizikova, Avneesh Sud +2
Many applications in 3D shape design and augmentation require the ability to make specific edits to an object's semantic parameters (e.g., the pose of a person's arm or the length…
SymmetryNet: Learning to Predict Reflectional and Rotational Symmetries of 3D Shapes from Single-View RGB-D Images
Yifei Shi, Junwen Huang, Hongjia Zhang +3
We study the problem of symmetry detection of 3D shapes from single-view RGB-D images, where severely missing data renders geometric detection approach infeasible. We propose an en…
Efficient Spatially Adaptive Convolution and Correlation
Thomas W. Mitchel, Benedict Brown, David Koller +3
Fast methods for convolution and correlation underlie a variety of applications in computer vision and graphics, including efficient filtering, analysis, and simulation. However, s…
PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction
Yifei Shi, Kai Xu, Matthias Niessner +2
We introduce a novel RGB-D patch descriptor designed for detecting coplanar surfaces in SLAM reconstruction. The core of our method is a deep convolutional neural net that takes in…