206 citations · 430 across the 20 of their papers we have counts for
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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…
Spatial Action Maps for Mobile Manipulation
Jimmy Wu, Xingyuan Sun, Andy Zeng +4
Typical end-to-end formulations for learning robotic navigation involve predicting a small set of steering command actions (e.g., step forward, turn left, turn right, etc.) from im…