6 papers
Relative Energy Learning for LiDAR Out-of-Distribution Detection
Zizhao Li, Zhengkang Xiang, Jiayang Ao +2
Out-of-distribution (OOD) detection is a critical requirement for reliable autonomous driving, where safety depends on recognizing road obstacles and unexpected objects beyond the…
Neural Distribution Prior for LiDAR Out-of-Distribution Detection
Zizhao Li, Zhengkang Xiang, Jiayang Ao +3
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 assumpt…
Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detection
Xueyang Kang, Zizhao Li, Tian Lan +3
3D shape anomaly detection is a crucial task for industrial inspection and geometric analysis. Existing deep learning approaches typically learn representations of normal shapes an…
From Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel Objects
Zizhao Li, Zhengkang Xiang, Joseph West +1
Traditional object detection methods operate under the closed-set assumption, where models can only detect a fixed number of objects predefined in the training set. Recent works on…
Out-of-distribution detection in 3D applications: a review
Zizhao Li, Xueyang Kang, Joseph West +1
The ability to detect objects that are not prevalent in the training set is a critical capability in many 3D applications, including autonomous driving. Machine learning methods fo…
SG-LDM: Semantic-Guided LiDAR Generation via Latent-Aligned Diffusion
Zhengkang Xiang, Zizhao Li, Amir Khodabandeh +1
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 diversit…