activity
20232025
most citedMasked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos

2 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2025

Re-Densification Meets Cross-Scale Propagation: Real-Time Neural Compression of LiDAR Point Clouds

Pengpeng Yu, Haoran Li, Runqing Jiang +3

LiDAR point clouds are fundamental to various applications, yet high-precision scans incur substantial storage and transmission overhead. Existing methods typically convert unorder…

cs.CV2025

A Unified Hierarchical Framework for Fine-grained Cross-view Geo-localization over Large-scale Scenarios

Zhuo Song, Ye Zhang, Kunhong Li +2

Cross-view geo-localization is a promising solution for large-scale localization problems, requiring the sequential execution of retrieval and metric localization tasks to achieve…

cs.CV2025

DropoutGS: Dropping Out Gaussians for Better Sparse-view Rendering

Yexing Xu, Longguang Wang, Minglin Chen +3

Although 3D Gaussian Splatting (3DGS) has demonstrated promising results in novel view synthesis, its performance degrades dramatically with sparse inputs and generates undesirable…

cs.CV20251 cited

SaMam: Style-aware State Space Model for Arbitrary Image Style Transfer

Hongda Liu, Longguang Wang, Ye Zhang +2

Global effective receptive field plays a crucial role for image style transfer (ST) to obtain high-quality stylized results. However, existing ST backbones (e.g., CNNs and Transfor…

cs.CV20241 cited

DuInNet: Dual-Modality Feature Interaction for Point Cloud Completion

Xinpu Liu, Baolin Hou, Hanyun Wang +3

To further promote the development of multimodal point cloud completion, we contribute a large-scale multimodal point cloud completion benchmark ModelNet-MPC with richer shape cate…

cs.CV20232 cited

Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos

Zhiqiang Shen, Xiaoxiao Sheng, Hehe Fan +5

Recently, the community has made tremendous progress in developing effective methods for point cloud video understanding that learn from massive amounts of labeled data. However, a…