60 citations · 95 across the 6 of their papers we have counts for
6 papers
Hyperspherical Embedding for Point Cloud Completion
Junming Zhang, Haomeng Zhang, Ram Vasudevan +1
Most real-world 3D measurements from depth sensors are incomplete, and to address this issue the point cloud completion task aims to predict the complete shapes of objects from par…
Learning Rotation-Invariant Representations of Point Clouds Using Aligned Edge Convolutional Neural Networks
Junming Zhang, Ming-Yuan Yu, Ram Vasudevan +1
Point cloud analysis is an area of increasing interest due to the development of 3D sensors that are able to rapidly measure the depth of scenes accurately. Unfortunately, applying…
Point Set Voting for Partial Point Cloud Analysis
Junming Zhang, Weijia Chen, Yuping Wang +2
The continual improvement of 3D sensors has driven the development of algorithms to perform point cloud analysis. In fact, techniques for point cloud classification and segmentatio…
LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery
Junming Zhang, Manikandasriram Srinivasan Ramanagopal, Ram Vasudevan +1
An accurate depth map of the environment is critical to the safe operation of autonomous robots and vehicles. Currently, either light detection and ranging (LIDAR) or stereo matchi…
DispSegNet: Leveraging Semantics for End-to-End Learning of Disparity Estimation from Stereo Imagery
Junming Zhang, Katherine A. Skinner, Ram Vasudevan +1
Recent work has shown that convolutional neural networks (CNNs) can be applied successfully in disparity estimation, but these methods still suffer from errors in regions of low-te…
Contractive De-noising Auto-encoder
Fu-qiang Chen, Yan Wu, Guo-dong Zhao +3
Auto-encoder is a special kind of neural network based on reconstruction. De-noising auto-encoder (DAE) is an improved auto-encoder which is robust to the input by corrupting the o…