57 citations · 64 across the 11 of their papers we have counts for
12 papers
PartLLM: A Unified Multimodal Foundation for 3D Part Segmentation
Zhe Zhu, Yiheng Zhang, Peng Li +9
Part segmentation is a fundamental problem in computer graphics and 3D vision. Recent works have expanded 3D part segmentation beyond fixed taxonomies, but existing approaches typi…
Efficient RWKV-based Representation Learning for 3D Point Clouds
Yun Liu, Xuefeng Yan, Liangliang Nan +5
The recent receptance weighted key value (RWKV) model combines RNN-style recurrence, offering a linear-complexity alternative to Transformers' quadratic self-attention for modeling…
BSGS: Bi-stage 3D Gaussian Splatting for Camera Motion Deblurring
An Zhao, Piaopiao Yu, Zhe Zhu +1
3D Gaussian Splatting has exhibited remarkable capabilities in 3D scene reconstruction. However, reconstructing high-quality 3D scenes from motion-blurred images caused by camera m…
CoreEditor: Correspondence-constrained Diffusion for Consistent 3D Editing
Zhe Zhu, Honghua Chen, Peng Li +1
Text-driven 3D editing seeks to modify 3D scenes according to textual descriptions, and most existing approaches tackle this by adapting pre-trained 2D image editors to multi-view…
BridgeShape: Latent Diffusion Schrödinger Bridge for 3D Shape Completion
Dequan Kong, Honghua Chen, Zhe Zhu +1
Existing diffusion-based 3D shape completion methods typically use a conditional paradigm, injecting incomplete shape information into the denoising network via deep feature intera…
PointSFDA: Source-free Domain Adaptation for Point Cloud Completion
Xing He, Zhe Zhu, Liangliang Nan +3
Conventional methods for point cloud completion, typically trained on synthetic datasets, face significant challenges when applied to out-of-distribution real-world scans. In this…
GenPC: Zero-shot Point Cloud Completion via 3D Generative Priors
An Li, Zhe Zhu, Mingqiang Wei
Existing point cloud completion methods, which typically depend on predefined synthetic training datasets, encounter significant challenges when applied to out-of-distribution, rea…
PointSea: Point Cloud Completion via Self-structure Augmentation
Zhe Zhu, Honghua Chen, Xing He +1
Point cloud completion is a fundamental yet not well-solved problem in 3D vision. Current approaches often rely on 3D coordinate information and/or additional data (e.g., images an…
SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator
Zhe Zhu, Honghua Chen, Xing He +3
In this paper, we propose a novel network, SVDFormer, to tackle two specific challenges in point cloud completion: understanding faithful global shapes from incomplete point clouds…
LBF:Learnable Bilateral Filter For Point Cloud Denoising
Huajian Si, Zeyong Wei, Zhe Zhu +4
Bilateral filter (BF) is a fast, lightweight and effective tool for image denoising and well extended to point cloud denoising. However, it often involves continual yet manual para…
CSDN: Cross-modal Shape-transfer Dual-refinement Network for Point Cloud Completion
Zhe Zhu, Liangliang Nan, Haoran Xie +4
How will you repair a physical object with some missings? You may imagine its original shape from previously captured images, recover its overall (global) but coarse shape first, a…
AGConv: Adaptive Graph Convolution on 3D Point Clouds
Mingqiang Wei, Zeyong Wei, Haoran Zhou +9
Convolution on 3D point clouds is widely researched yet far from perfect in geometric deep learning. The traditional wisdom of convolution characterises feature correspondences ind…