1 citations · 1 across the 14 of their papers we have counts for
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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…
PartSAM: A Scalable Promptable Part Segmentation Model Trained on Native 3D Data
Zhe Zhu, Le Wan, Rui Xu +6
Segmenting 3D objects into parts is a long-standing challenge in computer vision. To overcome taxonomy constraints and generalize to unseen 3D objects, recent works turn to open-wo…
ColorGS: High-fidelity Surgical Scene Reconstruction with Colored Gaussian Splatting
Qun Ji, Peng Li, Mingqiang Wei
High-fidelity reconstruction of deformable tissues from endoscopic videos remains challenging due to the limitations of existing methods in capturing subtle color variations and mo…
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…
RARE: Refine Any Registration of Pairwise Point Clouds via Zero-Shot Learning
Chengyu Zheng, Jin Huang, Honghua Chen +1
Recent research leveraging large-scale pretrained diffusion models has demonstrated the potential of using diffusion features to establish semantic correspondences in images. Inspi…
I Speak and You Find: Robust 3D Visual Grounding with Noisy and Ambiguous Speech Inputs
Yu Qi, Lipeng Gu, Honghua Chen +2
Existing 3D visual grounding methods rely on precise text prompts to locate objects within 3D scenes. Speech, as a natural and intuitive modality, offers a promising alternative. R…
Unified Representation Space for 3D Visual Grounding
Yinuo Zheng, Lipeng Gu, Honghua Chen +2
3D visual grounding (3DVG) is a critical task in scene understanding that aims to identify objects in 3D scenes based on text descriptions. However, existing methods rely on separa…
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…
SceneSplat++: A Large Dataset and Comprehensive Benchmark for Language Gaussian Splatting
Mengjiao Ma, Qi Ma, Yue Li +10
3D Gaussian Splatting (3DGS) serves as a highly performant and efficient encoding of scene geometry, appearance, and semantics. Moreover, grounding language in 3D scenes has proven…
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…
STAR-Edge: Structure-aware Local Spherical Curve Representation for Thin-walled Edge Extraction from Unstructured Point Clouds
Zikuan Li, Honghua Chen, Yuecheng Wang +3
Extracting geometric edges from unstructured point clouds remains a significant challenge, particularly in thin-walled structures that are commonly found in everyday objects. Tradi…
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…