activity
20152022
most cited1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

12 citations · 22 across the 8 of their papers we have counts for

collaborators

16 papers

cs.CV202212 cited

1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

Benjamin Kiefer, Matej Kristan, Janez Perš +70

The 1 Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and…

cs.CV2022

PointInverter: Point Cloud Reconstruction and Editing via a Generative Model with Shape Priors

Jaeyeon Kim, Binh-Son Hua, Duc Thanh Nguyen +1

In this paper, we propose a new method for mapping a 3D point cloud to the latent space of a 3D generative adversarial network. Our generative model for 3D point clouds is based on…

cs.CV2022

Time-of-Day Neural Style Transfer for Architectural Photographs

Yingshu Chen, Tuan-Anh Vu, Ka-Chun Shum +2

Architectural photography is a genre of photography that focuses on capturing a building or structure in the foreground with dramatic lighting in the background. Inspired by recent…

cs.CV2022

RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning

Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung

3D point clouds deep learning is a promising field of research that allows a neural network to learn features of point clouds directly, making it a robust tool for solving 3D scene…

cs.RO20202 cited

Dual-SLAM: A framework for robust single camera navigation

Huajian Huang, Wen-Yan Lin, Siying Liu +2

SLAM (Simultaneous Localization And Mapping) seeks to provide a moving agent with real-time self-localization. To achieve real-time speed, SLAM incrementally propagates position es…

cs.CV20205 cited

Global Context Aware Convolutions for 3D Point Cloud Understanding

Zhiyuan Zhang, Binh-Son Hua, Wei Chen +2

Recent advances in deep learning for 3D point clouds have shown great promises in scene understanding tasks thanks to the introduction of convolution operators to consume 3D point…