187 citations · 222 across the 22 of their papers we have counts for
23 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…
Universal 3D Shape Matching via Coarse-to-Fine Language Guidance
Qinfeng Xiao, Guofeng Mei, Bo Yang +3
Establishing dense correspondences between shapes is a crucial task in computer vision and graphics, while prior approaches depend on near-isometric assumptions and homogeneous sub…
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…
Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion
Wentao Qu, Guofeng Mei, Jing Wang +3
Denoising Diffusion Probabilistic Models (DDPMs) have shown success in robust 3D object detection tasks. Existing methods often rely on the score matching from 3D boxes or pre-trai…
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…
PointGAC: Geometric-Aware Codebook for Masked Point Cloud Modeling
Abiao Li, Chenlei Lv, Yuming Fang +3
Most masked point cloud modeling (MPM) methods follow a regression paradigm to reconstruct the coordinate or feature of masked regions. However, they tend to over-constrain the mod…
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…
GSTran: Joint Geometric and Semantic Coherence for Point Cloud Segmentation
Abiao Li, Chenlei Lv, Guofeng Mei +3
Learning meaningful local and global information remains a challenge in point cloud segmentation tasks. When utilizing local information, prior studies indiscriminately aggregates…
Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning
Bin Ren, Guofeng Mei, Danda Pani Paudel +6
Contrastive learning (CL) for Vision Transformers (ViTs) in image domains has achieved performance comparable to CL for traditional convolutional backbones. However, in 3D point cl…
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…
Geometrically-driven Aggregation for Zero-shot 3D Point Cloud Understanding
Guofeng Mei, Luigi Riz, Yiming Wang +1
Zero-shot 3D point cloud understanding can be achieved via 2D Vision-Language Models (VLMs). Existing strategies directly map Vision-Language Models from 2D pixels of rendered or c…
Point Cloud Pre-training with Diffusion Models
Xiao Zheng, Xiaoshui Huang, Guofeng Mei +5
Pre-training a model and then fine-tuning it on downstream tasks has demonstrated significant success in the 2D image and NLP domains. However, due to the unordered and non-uniform…
Attentive Multimodal Fusion for Optical and Scene Flow
Youjie Zhou, Guofeng Mei, Yiming Wang +2
This paper presents an investigation into the estimation of optical and scene flow using RGBD information in scenarios where the RGB modality is affected by noise or captured in da…
Cross-source Point Cloud Registration: Challenges, Progress and Prospects
Xiaoshui Huang, Guofeng Mei, Jian Zhang
The emerging topic of cross-source point cloud (CSPC) registration has attracted increasing attention with the fast development background of 3D sensor technologies. Different from…
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…
Graph Matching Optimization Network for Point Cloud Registration
Qianliang Wu, Yaqi Shen, Haobo Jiang +5
Point Cloud Registration is a fundamental and challenging problem in 3D computer vision. Recent works often utilize the geometric structure information in point feature embedding o…
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…
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…
GenReg: Deep Generative Method for Fast Point Cloud Registration
Xiaoshui Huang, Zongyi Xu, Guofeng Mei +4
Accurate and efficient point cloud registration is a challenge because the noise and a large number of points impact the correspondence search. This challenge is still a remaining…
A comprehensive survey on point cloud registration
Xiaoshui Huang, Guofeng Mei, Jian Zhang +1
Registration is a transformation estimation problem between two point clouds, which has a unique and critical role in numerous computer vision applications. The developments of opt…
Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences
Xiaoshui Huang, Guofeng Mei, Jian Zhang
We present a fast feature-metric point cloud registration framework, which enforces the optimisation of registration by minimising a feature-metric projection error without corresp…