Guangzhi Cao — accepted AI-conference papers

2026

S2D: Sparse to Dense Lifting for 3D Reconstruction with Minimal Inputs

Yuzhou Ji, Qijian Tian, He Zhu, Xiaoqi Jiang, Guangzhi Cao, Lizhuang Ma +2

CVPR 2026

Explicit 3D representations have already become an essential medium for 3D simulation and understanding. However, the most commonly used point cloud and 3D Gaussian Splatting (3DGS) each suffer from non-photorealistic rendering and significant degradation under sparse inputs. In this paper, we intro

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2024

Improving Radial Imbalances with Hybrid Voxelization and RadialMix for LiDAR 3D Semantic Segmentation

Jiale Li, Hang Dai, Yu Wang, Guangzhi Cao, Chun Luo, Yong Ding

ICRA 2024poster

Huge progress has been made in LiDAR 3D semantic segmentation, but there are two under-explored imbalances on the radial axis: points are unevenly concentrated on the near side, and the distribution of foreground object instances is skewed to the near side. This leads the training of the model to fa…

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