12 citations · 17 across the 15 of their papers we have counts for
15 papers
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,…
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…
Parameter-Efficient CLIP Adaptation for 3D Understanding via Unified Tokenization
Guofeng Mei, Qinfeng Xiao, Bin Ren +7
Vision-language models, such as CLIP, encode rich semantic knowledge through large-scale image-text pretraining. Reusing these models for 3D understanding is highly desirable, beca…
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…
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…
Novel class discovery meets foundation models for 3D semantic segmentation
Luigi Riz, Cristiano Saltori, Yiming Wang +2
The task of Novel Class Discovery (NCD) in semantic segmentation entails training a model able to accurately segment unlabelled (novel) classes, relying on the available supervisio…
ZeroReg: Zero-Shot Point Cloud Registration with Foundation Models
Weijie Wang, Wenqi Ren, Guofeng Mei +5
State-of-the-art 3D point cloud registration methods rely on labeled 3D datasets for training, which limits their practical applications in real-world scenarios and often hinders g…
Delving into CLIP latent space for Video Anomaly Recognition
Luca Zanella, Benedetta Liberatori, Willi Menapace +3
We tackle the complex problem of detecting and recognising anomalies in surveillance videos at the frame level, utilising only video-level supervision. We introduce the novel metho…
Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation
Cristiano Saltori, Fabio Galasso, Giuseppe Fiameni +3
Deep-learning models for 3D point cloud semantic segmentation exhibit limited generalization capabilities when trained and tested on data captured with different sensors or in vary…
Novel Class Discovery for 3D Point Cloud Semantic Segmentation
Luigi Riz, Cristiano Saltori, Elisa Ricci +1
Novel class discovery (NCD) for semantic segmentation is the task of learning a model that can segment unlabelled (novel) classes using only the supervision from labelled (base) cl…
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…
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…
Wave equation for Sturm-Liouville operator with singular intermediate coefficient and potential
Michael Ruzhansky, Alibek Yeskermessuly
In this paper, we consider a wave equation on a bounded domain with a Sturm-Liouville operator with a singular intermediate coefficient and a singular potential. To obtain and eval…
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation
Cristiano Saltori, Fabio Galasso, Giuseppe Fiameni +3
3D LiDAR semantic segmentation is fundamental for autonomous driving. Several Unsupervised Domain Adaptation (UDA) methods for point cloud data have been recently proposed to impro…
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation
Cristiano Saltori, Evgeny Krivosheev, Stéphane Lathuilière +5
3D point cloud semantic segmentation is fundamental for autonomous driving. Most approaches in the literature neglect an important aspect, i.e., how to deal with domain shift when…