2 citations · 3 across the 6 of their papers we have counts for
6 papers
Rethinking Few-shot 3D Point Cloud Semantic Segmentation
Zhaochong An, Guolei Sun, Yun Liu +5
This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS), with a focus on two significant issues in the state-of-the-art: foreground leakage and sparse point dist…
TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding
Yun Liu, Haolin Yang, Xu Si +5
Humans commonly work with multiple objects in daily life and can intuitively transfer manipulation skills to novel objects by understanding object functional regularities. However,…
Efficient Polyp Segmentation Via Integrity Learning
Ziqiang Chen, Kang Wang, Yun Liu
Accurate polyp delineation in colonoscopy is crucial for assisting in diagnosis, guiding interventions, and treatments. However, current deep-learning approaches fall short due to…
UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning
Weikang Wan, Haoran Geng, Yun Liu +4
We propose a novel, object-agnostic method for learning a universal policy for dexterous object grasping from realistic point cloud observations and proprioceptive information unde…
PointGame: Geometrically and Adaptively Masked Auto-Encoder on Point Clouds
Yun Liu, Xuefeng Yan, Zhilei Chen +3
Self-supervised learning is attracting large attention in point cloud understanding. However, exploring discriminative and transferable features still remains challenging due to th…
Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes
Yu-Huan Wu, Da Zhang, Le Zhang +4
Current efficient LiDAR-based detection frameworks are lacking in exploiting object relations, which naturally present in both spatial and temporal manners. To this end, we introdu…