Search Results for author: Shubham Agrawal

Found 9 papers, 3 papers with code

Joint Liver and Hepatic Lesion Segmentation using a Hybrid CNN with Transformer Layers

no code implementations26 Jan 2022 Georg Hille, Shubham Agrawal, Christian Wybranski, Maciej Pech, Alexey Surov, Sylvia Saalfeld

This network was applied to clinical liver MRI, as well as to the publicly available CT data of the liver tumor segmentation (LiTS) challenge.

Lesion Segmentation Tumor Segmentation

Scene Editing as Teleoperation: A Case Study in 6DoF Kit Assembly

no code implementations9 Oct 2021 Shubham Agrawal, Yulong Li, Jen-Shuo Liu, Steven K. Feiner, Shuran Song

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

1 code implementation28 Nov 2020 Zhenjia Xu, Beichun Qi, Shubham Agrawal, Shuran Song

We propose AdaGrasp, a method to learn a single grasping policy that generalizes to novel grippers.


Fit2Form: 3D Generative Model for Robot Gripper Form Design

1 code implementation12 Nov 2020 Huy Ha, Shubham Agrawal, Shuran Song

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.

Automatic Differentiation and Continuous Sensitivity Analysis of Rigid Body Dynamics

no code implementations22 Jan 2020 David Millard, Eric Heiden, Shubham Agrawal, Gaurav S. Sukhatme

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.

Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistency

1 code implementation7 May 2019 Tejas Khot, Shubham Agrawal, Shubham Tulsiani, Christoph Mertz, Simon Lucey, Martial Hebert

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.

Depth Estimation

High Fidelity Semantic Shape Completion for Point Clouds using Latent Optimization

no code implementations9 Jul 2018 Swaminathan Gurumurthy, Shubham Agrawal

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.

Computer Vision

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