3 citations · 7 across the 8 of their papers we have counts for
8 papers
T2LDM++: A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation
Wentao Qu, Qi Zhang, Chenxu Wang +5
Recent progress in Text-to-Image generation benefits from large-scale Text-Image pairs. However, the scarcity of Text-LiDAR pairs often causes over-smoothed scenes and limited cont…
A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation
Wentao Qu, Guofeng Mei, Yang Wu +3
Text-to-LiDAR generation can customize 3D data with rich structures and diverse scenes for downstream tasks. However, the scarcity of Text-LiDAR pairs often causes insufficient tra…
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…
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…
Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration
Guofeng Mei, Hao Tang, Xiaoshui Huang +5
Deep point cloud registration methods face challenges to partial overlaps and rely on labeled data. To address these issues, we propose UDPReg, an unsupervised deep probabilistic r…
Unsupervised Point Cloud Pre-Training via Contrasting and Clustering
Guofeng Mei, Xiaoshui Huang, Juan Liu +2
Annotating large-scale point clouds is highly time-consuming and often infeasible for many complex real-world tasks. Point cloud pre-training has therefore become a promising strat…
COTReg:Coupled Optimal Transport based Point Cloud Registration
Guofeng Mei, Xiaoshui Huang, Litao Yu +2
Generating a set of high-quality correspondences or matches is one of the most critical steps in point cloud registration. This paper proposes a learning framework COTReg by jointl…