You Only Scan Once: A Dynamic Scene Reconstruction Pipeline for 6-DoF Robotic Grasping of Novel Objects

no code implementations • 4 Apr 2024 • Lei Zhou, Haozhe Wang, Zhengshen Zhang, Zhiyang Liu, Francis EH Tay, adn Marcelo H. Ang. Jr

In the realm of robotic grasping, achieving accurate and reliable interactions with the environment is a pivotal challenge.

Object Pose Tracking +2

DR-Pose: A Two-stage Deformation-and-Registration Pipeline for Category-level 6D Object Pose Estimation

1 code implementation • 5 Sep 2023 • Lei Zhou, Zhiyang Liu, Runze Gan, Haozhe Wang, Marcelo H. Ang Jr

In the second stage, a novel registration network is designed to extract pose-sensitive features and predict the representation of object partial point cloud in canonical space based on the deformation results from the first stage.

6D Pose Estimation using RGB Object +1

The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

1 code implementation • 5 Jul 2023 • Nicholas Heller, Fabian Isensee, Dasha Trofimova, Resha Tejpaul, Zhongchen Zhao, Huai Chen, Lisheng Wang, Alex Golts, Daniel Khapun, Daniel Shats, Yoel Shoshan, Flora Gilboa-Solomon, Yasmeen George, Xi Yang, Jianpeng Zhang, Jing Zhang, Yong Xia, Mengran Wu, Zhiyang Liu, Ed Walczak, Sean McSweeney, Ranveer Vasdev, Chris Hornung, Rafat Solaiman, Jamee Schoephoerster, Bailey Abernathy, David Wu, Safa Abdulkadir, Ben Byun, Justice Spriggs, Griffin Struyk, Alexandra Austin, Ben Simpson, Michael Hagstrom, Sierra Virnig, John French, Nitin Venkatesh, Sarah Chan, Keenan Moore, Anna Jacobsen, Susan Austin, Mark Austin, Subodh Regmi, Nikolaos Papanikolopoulos, Christopher Weight

Overall KiTS21 facilitated a significant advancement in the state of the art in kidney tumor segmentation, and provides useful insights that are applicable to the field of semantic segmentation as a whole.

Segmentation Tumor Segmentation

PEGG-Net: Pixel-Wise Efficient Grasp Generation in Complex Scenes

1 code implementation • 30 Mar 2022 • Haozhe Wang, Zhiyang Liu, Lei Zhou, Huan Yin, Marcelo H Ang Jr

Vision-based grasp estimation is an essential part of robotic manipulation tasks in the real world.

Grasp Generation

Automated Segmentation of Brain Gray Matter Nuclei on Quantitative Susceptibility Mapping Using Deep Convolutional Neural Network

no code implementations • 3 Aug 2020 • Chao Chai, Pengchong Qiao, Bin Zhao, Huiying Wang, Guohua Liu, Hong Wu, E Mark Haacke, Wen Shen, Chen Cao, Xinchen Ye, Zhiyang Liu, Shuang Xia

Abnormal iron accumulation in the brain subcortical nuclei has been reported to be correlated to various neurodegenerative diseases, which can be measured through the magnetic susceptibility from the quantitative susceptibility mapping (QSM).

Fast and Accurate Optical Fiber Channel Modeling Using Generative Adversarial Network

no code implementations • 28 Feb 2020 • Hang Yang, Zekun Niu, Shilin Xiao, Jiafei Fang, Zhiyang Liu, David Faninsin, Lilin Yi

In this work, a new data-driven fiber channel modeling method, generative adversarial network (GAN) is investigated to learn the distribution of fiber channel transfer function.

Generative Adversarial Network

Automatic acute ischemic stroke lesion segmentation using semi-supervised learning

no code implementations • 10 Aug 2019 • Bin Zhao, Shuxue Ding, Hong Wu, Guohua Liu, Chen Cao, Song Jin, Zhiyang Liu

By using a large number of weakly labeled subjects and a small number of fully labeled subjects, our proposed method is able to accurately detect and segment the AIS lesions.

Clustering Ischemic Stroke Lesion Segmentation +1

Towards Clinical Diagnosis: Automated Stroke Lesion Segmentation on Multimodal MR Image Using Convolutional Neural Network

no code implementations • 5 Mar 2018 • Zhiyang Liu, Chen Cao, Shuxue Ding, Tong Han, Hong Wu, Sheng Liu

The patient with ischemic stroke can benefit most from the earliest possible definitive diagnosis.

Lesion Segmentation