Runnan Huang — accepted AI-conference papers

2026

Perceive More with Less: LiDAR Point Cloud Compression at Just Recognizable Distortion for 3D Scene Understanding

Miaohui Wang, Runnan Huang, Taojun Liu, Shuyuan Lin, Ye Liu, Yun Song

AAAI 2026technical

Existing LiDAR point cloud (LPC) data coding methods primarily focus on balancing compression efficiency and reconstruction quality according to the human vision system (HVS). However, these methods rarely consider the requirements of downstream scene understanding tasks from the perspective of the

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2024

msLPCC: A Multimodal-Driven Scalable Framework for Deep LiDAR Point Cloud Compression

Miaohui Wang, Runnan Huang, Hengjin Dong, Di Lin, Yun Song, Wuyuan Xie

AAAI 2024technical

LiDAR sensors are widely used in autonomous driving, and the growing storage and transmission demands have made LiDAR point cloud compression (LPCC) a hot research topic. To address the challenges posed by the large-scale and uneven-distribution (spatial and categorical) of LiDAR point data, this pa…

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