4 citations · 7 across the 10 of their papers we have counts for
9 papers · 1 filter
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…
LAHNet: Local Attentive Hashing Network for Point Cloud Registration
Wentao Qu, Xiaoshui Huang, Liang Xiao
Most existing learning-based point cloud descriptors for point cloud registration focus on perceiving local information of point clouds to generate distinctive features. However, a…
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…
An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Models
Wentao Qu, Jing Wang, YongShun Gong +2
Existing conditional Denoising Diffusion Probabilistic Models (DDPMs) with a Noise-Conditional Framework (NCF) remain challenging for 3D scene understanding tasks, as the complex g…
A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling
Wentao Qu, Yuantian Shao, Lingwu Meng +2
Point cloud upsampling (PCU) enriches the representation of raw point clouds, significantly improving the performance in downstream tasks such as classification and reconstruction.…
EPCL: Frozen CLIP Transformer is An Efficient Point Cloud Encoder
Xiaoshui Huang, Zhou Huang, Sheng Li +5
The pretrain-finetune paradigm has achieved great success in NLP and 2D image fields because of the high-quality representation ability and transferability of their pretrained mode…
GMF: General Multimodal Fusion Framework for Correspondence Outlier Rejection
Xiaoshui Huang, Wentao Qu, Yifan Zuo +2
Rejecting correspondence outliers enables to boost the correspondence quality, which is a critical step in achieving high point cloud registration accuracy. The current state-of-th…
IMFNet: Interpretable Multimodal Fusion for Point Cloud Registration
Xiaoshui Huang, Wentao Qu, Yifan Zuo +2
The existing state-of-the-art point descriptor relies on structure information only, which omit the texture information. However, texture information is crucial for our humans to d…