37 citations · 40 across the 8 of their papers we have counts for
7 papers · 1 filter
FullPart: Generating each 3D Part at Full Resolution
Lihe Ding, Shaocong Dong, Yaokun Li +10
Part-based 3D generation holds great potential for various applications. Previous part generators that represent parts using implicit vector-set tokens often suffer from insufficie…
From One to More: Contextual Part Latents for 3D Generation
Shaocong Dong, Lihe Ding, Xiao Chen +10
Recent advances in 3D generation have transitioned from multi-view 2D rendering approaches to 3D-native latent diffusion frameworks that exploit geometric priors in ground truth da…
MsSVT++: Mixed-scale Sparse Voxel Transformer with Center Voting for 3D Object Detection
Jianan Li, Shaocong Dong, Lihe Ding +1
Accurate 3D object detection in large-scale outdoor scenes, characterized by considerable variations in object scales, necessitates features rich in both long-range and fine-graine…
Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors
Lihe Ding, Shaocong Dong, Zhanpeng Huang +5
Most 3D generation research focuses on up-projecting 2D foundation models into the 3D space, either by minimizing 2D Score Distillation Sampling (SDS) loss or fine-tuning on multi-…
Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions
Jie Wang, Lihe Ding, Tingfa Xu +4
Robust 3D perception under corruption has become an essential task for the realm of 3D vision. While current data augmentation techniques usually perform random transformations on…
CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds
Haiyang Wang, Lihe Ding, Shaocong Dong +5
We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D. Our proposed method first generates some high-quality 3D proposals by levera…