Search Results for author:
Found 9 papers, 3 papers with code
Joint Liver and Hepatic Lesion Segmentation using a Hybrid CNN with Transformer Layers
This network was applied to clinical liver MRI, as well as to the publicly available CT data of the liver tumor segmentation (LiTS) challenge.
Scene Editing as Teleoperation: A Case Study in 6DoF Kit Assembly
To make teleoperation accessible to non-expert users, we propose the framework "Scene Editing as Teleoperation" (SEaT), where the key idea is to transform the traditional "robot-centric" interface into a "scene-centric" interface -- instead of controlling the robot, users focus on specifying the task's goal by manipulating digital twins of the real-world objects.
AdaGrasp: Learning an Adaptive Gripper-Aware Grasping Policy
We propose AdaGrasp, a method to learn a single grasping policy that generalizes to novel grippers.
Robotics
Fit2Form: 3D Generative Model for Robot Gripper Form Design
We propose Fit2Form, a 3D generative design framework that generates pairs of finger shapes to maximize design objectives (i. e., grasp success, stability, and robustness) for target grasp objects.
High Accuracy Face Geometry Capture using a Smartphone Video
What's the most accurate 3D model of your face you can obtain while sitting at your desk?
Automatic Differentiation and Continuous Sensitivity Analysis of Rigid Body Dynamics
A key ingredient to achieving intelligent behavior is physical understanding that equips robots with the ability to reason about the effects of their actions in a dynamic environment.
Interactive Image Generation Using Scene Graphs
We propose a method to generate an image incrementally based on a sequence of graphs of scene descriptions (scene-graphs).
Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistency
We demonstrate our ability to learn MVS without 3D supervision using a real dataset, and show that each component of our proposed robust loss results in a significant improvement.
High Fidelity Semantic Shape Completion for Point Clouds using Latent Optimization
Experiments show that our algorithm is capable of successfully reconstructing point clouds with large missing regions with very high fidelity without having to rely on exemplar based database retrieval.
