Wenbang Deng — accepted AI-conference papers

2025

A Novel Decomposed Feature-Oriented Framework for Open-Set Semantic Segmentation on LiDAR Data

Wenbang Deng, Xieyuanli Chen, Qinghua Yu, Yunze He, Junhao Xiao, Huimin Lu

ICRA 2025

Semantic segmentation is a key technique that enables mobile robots to understand and navigate surrounding environments autonomously. However, most existing works focus on segmenting known objects, overlooking the identification of unknown classes, which is common in real-world applications. In this

Cited by 1 Source ↗code ↗Scholar ↗

2023

ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data

Wenbang Deng, Kaihong Huang, Qinghua Yu, Huimin Lu, Zhiqiang Zheng, Xieyuanli Chen

IROS 2023poster

Open-world Instance Segmentation (OIS) is a challenging task that aims to accurately segment every object instance appearing in the current observation, regardless of whether these instances have been labeled in the training set. This is important for safety-critical applications such as robust auto…

Cited by 4 Source ↗code ↗Scholar ↗