activity
20182026
most citedACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action Localization

44 citations · 64 across the 17 of their papers we have counts for

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20 papers · 1 filter

cs.CV2026

Signal Structure-Aware Gaussian Splatting for Large-Scale Scene Reconstruction

Weiyi Xue, Fan Lu, Chi Zhang +6

3D Gaussian Splatting has demonstrated remarkable potential in novel view synthesis. In contrast to small-scale scenes, large-scale scenes inevitably contain sparsely observed regi…

cs.CV2026

MU-GeNeRF: Multi-view Uncertainty-guided Generalizable Neural Radiance Fields for Distractor-aware Scene

Wenjie Mu, Zhan Li, Chuanzhou Su +8

Generalizable Neural Radiance Fields (GeNeRFs) enable high-quality scene reconstruction from sparse views and can generalize to unseen scenes. However, in real-world settings, tran…

cs.CV2025

LiDARDraft: Generating LiDAR Point Cloud from Versatile Inputs

Haiyun Wei, Fan Lu, Yunwei Zhu +6

Generating realistic and diverse LiDAR point clouds is crucial for autonomous driving simulation. Although previous methods achieve LiDAR point cloud generation from user inputs, t…

cs.CV2025

UrbanCraft: Urban View Extrapolation via Hierarchical Sem-Geometric Priors

Tianhang Wang, Fan Lu, Sanqing Qu +5

Existing neural rendering-based urban scene reconstruction methods mainly focus on the Interpolated View Synthesis (IVS) setting that synthesizes from views close to training camer…

cs.CV2025

R2LDM: An Efficient 4D Radar Super-Resolution Framework Leveraging Diffusion Model

Boyuan Zheng, Shouyi Lu, Renbo Huang +5

We introduce R2LDM, an innovative approach for generating dense and accurate 4D radar point clouds, guided by corresponding LiDAR point clouds. Instead of utilizing range images or…

cs.CV2025

Range and Bird's Eye View Fused Cross-Modal Visual Place Recognition

Jianyi Peng, Fan Lu, Bin Li +3

Image-to-point cloud cross-modal Visual Place Recognition (VPR) is a challenging task where the query is an RGB image, and the database samples are LiDAR point clouds. Compared to…