224 citations · 376 across the 6 of their papers we have counts for
6 papers
Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling
Qingyong Hu, Bo Yang, Linhai Xie +5
We study the problem of efficient semantic segmentation of large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing ste…
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
Qingyong Hu, Bo Yang, Linhai Xie +5
We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing st…
Learning with Stochastic Guidance for Navigation
Linhai Xie, Yishu Miao, Sen Wang +5
Due to the sparse rewards and high degree of environment variation, reinforcement learning approaches such as Deep Deterministic Policy Gradient (DDPG) are plagued by issues of hig…
Neural Allocentric Intuitive Physics Prediction from Real Videos
Zhihua Wang, Stefano Rosa, Yishu Miao +4
Humans are able to make rich predictions about the future dynamics of physical objects from a glance. On the other hand, most existing computer vision approaches require strong ass…
3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations
Zhihua Wang, Stefano Rosa, Bo Yang +3
The ability to interact and understand the environment is a fundamental prerequisite for a wide range of applications from robotics to augmented reality. In particular, predicting…
Defo-Net: Learning Body Deformation using Generative Adversarial Networks
Zhihua Wang, Stefano Rosa, Linhai Xie +4
Modelling the physical properties of everyday objects is a fundamental prerequisite for autonomous robots. We present a novel generative adversarial network (Defo-Net), able to pre…