6 papers
EPSegFZ: Efficient Point Cloud Semantic Segmentation for Few- and Zero-Shot Scenarios with Language Guidance
Jiahui Wang, Haiyue Zhu, Haoren Guo +3
Recent approaches for few-shot 3D point cloud semantic segmentation typically require a two-stage learning process, i.e., a pre-training stage followed by a few-shot training stage…
SingRef6D: Monocular Novel Object Pose Estimation with a Single RGB Reference
Jiahui Wang, Haiyue Zhu, Haoren Guo +3
Recent 6D pose estimation methods demonstrate notable performance but still face some practical limitations. For instance, many of them rely heavily on sensor depth, which may fail…
VLM-UDMC: VLM-Enhanced Unified Decision-Making and Motion Control for Urban Autonomous Driving
Haichao Liu, Haoren Guo, Pei Liu +4
Scene understanding and risk-aware attentions are crucial for human drivers to make safe and effective driving decisions. To imitate this cognitive ability in urban autonomous driv…
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention
Jiahui Wang, Haiyue Zhu, Haoren Guo +3
This paper presents an effective few-shot point cloud semantic segmentation approach for real-world applications. Existing few-shot segmentation methods on point cloud heavily rely…
CAM/CAD Point Cloud Part Segmentation via Few-Shot Learning
Jiahui Wang, Haiyue Zhu, Haoren Guo +3
3D part segmentation is an essential step in advanced CAM/CAD workflow. Precise 3D segmentation contributes to lower defective rate of work-pieces produced by the manufacturing equ…
Masked Self-Supervision for Remaining Useful Lifetime Prediction in Machine Tools
Haoren Guo, Haiyue Zhu, Jiahui Wang +3
Prediction of Remaining Useful Lifetime(RUL) in the modern manufacturing and automation workplace for machines and tools is essential in Industry 4.0. This is clearly evident as co…