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Qinghua Yu

4 accepted papers

2025

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

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 1SourcecodeScholar
2025

BEVDiffLoc: End-to-End LiDAR Global Localization in BEV View based on Diffusion Model

IROS 2025

Localization is one of the core parts of modern robotics. Classic localization methods typically follow the retrieve-then-register paradigm, achieving remarkable success. Recently, the emergence of end-to-end localization approaches has offered distinct advantages, including a streamlined system arc

Cited by 1SourcecodeScholar
2023

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

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 4SourcecodeScholar