Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox
Picture for Kaichen Zhou

Kaichen Zhou
Alert button

Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR


Sep 21, 2022
Sangyun Shin, Stuart Golodetz, Madhu Vankadari, Kaichen Zhou, Andrew Markham, Niki Trigoni

Add code
Alert button

Figure 1 for Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR
Figure 2 for Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR
Figure 3 for Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR
Figure 4 for Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction


Sep 20, 2022
Kaichen Zhou, Lanqing Hong, Changhao Chen, Hang Xu, Chaoqiang Ye, Qingyong Hu, Zhenguo Li

Add code
Alert button

Figure 1 for DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction
Figure 2 for DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction
Figure 3 for DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction
Figure 4 for DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction
* Accepted by European Conference on Computer Vision 2022 (ECCV2022) 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces


Mar 23, 2022
Jia-Xing Zhong, Kaichen Zhou, Qingyong Hu, Bing Wang, Niki Trigoni, Andrew Markham

Add code
Alert button

Figure 1 for No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces
Figure 2 for No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces
Figure 3 for No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces
Figure 4 for No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces
* To appear at CVPR 2022 (Source Code: https://github.com/jx-zhong-for-academic-purpose/Kinet ) 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture


Sep 13, 2021
Kaichen Zhou, Lanqing Hong, Shoukang Hu, Fengwei Zhou, Binxin Ru, Jiashi Feng, Zhenguo Li

Add code
Alert button

Figure 1 for DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture
Figure 2 for DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture
Figure 3 for DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture
Figure 4 for DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling


Jul 03, 2020
Chengliang Dai, Shuo Wang, Yuanhan Mo, Kaichen Zhou, Elsa Angelini, Yike Guo, Wenjia Bai

Add code
Alert button

Figure 1 for Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling
Figure 2 for Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling
Figure 3 for Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling
Figure 4 for Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling
* Paper accepted by MICCAI 2020 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email