2 citations · 11 across the 10 of their papers we have counts for
5 papers · 1 filter
MotionDeltaCNN: Sparse CNN Inference of Frame Differences in Moving Camera Videos
Mathias Parger, Chengcheng Tang, Thomas Neff +4
Convolutional neural network inference on video input is computationally expensive and requires high memory bandwidth. Recently, DeltaCNN managed to reduce the cost by only process…
Neural Correspondence Field for Object Pose Estimation
Lin Huang, Tomas Hodan, Lingni Ma +7
We propose a method for estimating the 6DoF pose of a rigid object with an available 3D model from a single RGB image. Unlike classical correspondence-based methods which predict 3…
Visual Pressure Estimation and Control for Soft Robotic Grippers
Patrick Grady, Jeremy A. Collins, Samarth Brahmbhatt +4
Soft robotic grippers facilitate contact-rich manipulation, including robust grasping of varied objects. Yet the beneficial compliance of a soft gripper also results in significant…
PressureVision: Estimating Hand Pressure from a Single RGB Image
Patrick Grady, Chengcheng Tang, Samarth Brahmbhatt +4
People often interact with their surroundings by applying pressure with their hands. While hand pressure can be measured by placing pressure sensors between the hand and the enviro…
DeltaCNN: End-to-End CNN Inference of Sparse Frame Differences in Videos
Mathias Parger, Chengcheng Tang, Christopher D. Twigg +3
Convolutional neural network inference on video data requires powerful hardware for real-time processing. Given the inherent coherence across consecutive frames, large parts of a v…