Zhengkang Xiang — accepted AI-conference papers

2026

Neural Distribution Prior for LiDAR Out-of-Distribution Detection

Zizhao Li, Zhengkang Xiang, Jiayang Ao, Feng Liu, Joseph West, Kourosh Khoshelham

CVPR 2026

LiDAR-based perception is critical for autonomous driving due to its robustness to poor lighting and visibility conditions. Yet, current models operate under the closed-set assumption and often fail to recognize unexpected out-of-distribution (OOD) objects in the open world. Existing OOD scoring fun

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2025

SG-LDM: Semantic-Guided LiDAR Generation via Latent-Aligned Diffusion

Zhengkang Xiang, Zizhao Li, Amir Khodabandeh, Kourosh Khoshelham

ICCV 2025poster

Lidar point cloud synthesis based on generative models offers a promising solution to augment deep learning pipelines, particularly when real-world data is scarce or lacks diversity. By enabling flexible object manipulation, this synthesis approach can significantly enrich training datasets and enha…

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