10 citations · 20 across the 4 of their papers we have counts for
6 papers
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…
Accelerating Large-Kernel Convolution Using Summed-Area Tables
Linguang Zhang, Maciej Halber, Szymon Rusinkiewicz
Expanding the receptive field to capture large-scale context is key to obtaining good performance in dense prediction tasks, such as human pose estimation. While many state-of-the-…
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…