activity
20222026
most cited1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

12 citations · 13 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2026

GLASS: Graph and Vision-Language Assisted Semantic Shape Correspondence

Qinfeng Xiao, Guofeng Mei, Qilong Liu +5

Establishing dense correspondence across 3D shapes is crucial for fundamental downstream tasks, including texture transfer, shape interpolation, and robotic manipulation. However,…

cs.CV2025

Masked Clustering Prediction for Unsupervised Point Cloud Pre-training

Bin Ren, Xiaoshui Huang, Mengyuan Liu +4

Vision transformers (ViTs) have recently been widely applied to 3D point cloud understanding, with masked autoencoding as the predominant pre-training paradigm. However, the challe…

cs.CV2025

Cross-Modal and Uncertainty-Aware Agglomeration for Open-Vocabulary 3D Scene Understanding

Jinlong Li, Cristiano Saltori, Fabio Poiesi +1

The lack of a large-scale 3D-text corpus has led recent works to distill open-vocabulary knowledge from vision-language models (VLMs). However, these methods typically rely on a si…

cs.CV2025

Fully-Geometric Cross-Attention for Point Cloud Registration

Weijie Wang, Guofeng Mei, Jian Zhang +3

Point cloud registration approaches often fail when the overlap between point clouds is low due to noisy point correspondences. This work introduces a novel cross-attention mechani…

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.CV20221 cited

Overlap-guided Gaussian Mixture Models for Point Cloud Registration

Guofeng Mei, Fabio Poiesi, Cristiano Saltori +3

Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations. However, registering point cloud pairs i…