activity
20232026
most citedHDMNet: A Hierarchical Matching Network with Double Attention for Large-scale Outdoor LiDAR Point Cloud Registration

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

collaborators

6 papers

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

Pano360: Perspective to Panoramic Vision with Geometric Consistency

Zhengdong Zhu, Weiyi Xue, Zuyuan Yang +2

Prior panorama stitching approaches heavily rely on pairwise feature correspondences and are unable to leverage geometric consistency across multiple views. This leads to severe di…

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.CV2024★ 1 cited

GeoNLF: Geometry guided Pose-Free Neural LiDAR Fields

Weiyi Xue, Zehan Zheng, Fan Lu +3

Although recent efforts have extended Neural Radiance Fields (NeRF) into LiDAR point cloud synthesis, the majority of existing works exhibit a strong dependence on precomputed pose…

cs.CV2024

LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis

Zehan Zheng, Fan Lu, Weiyi Xue +2

Although neural radiance fields (NeRFs) have achieved triumphs in image novel view synthesis (NVS), LiDAR NVS remains largely unexplored. Previous LiDAR NVS methods employ a simple…

cs.CV2023★ 1 cited

HDMNet: A Hierarchical Matching Network with Double Attention for Large-scale Outdoor LiDAR Point Cloud Registration

Weiyi Xue, Fan Lu, Guang Chen

Outdoor LiDAR point clouds are typically large-scale and complexly distributed. To achieve efficient and accurate registration, emphasizing the similarity among local regions and p…