Shengkai Wu — accepted AI-conference papers

2024

EfficientDPS: Efficient and End-to-End Depth-aware Panoptic Segmentation

Shengkai Wu, Liangliang Ren, Linfeng Gao, Yupeng Li, Wenyu Liu

ICRA 2024poster

Depth-aware panoptic segmentation (DPS) combines image segmentation and monocular depth estimation in a single model to achieve semantic and geometry perception simultaneously. DPS task has important applications in the robot area but the previous DPS models are too heavy to be applied. Thus, we pro…

Cited by 1 Source ↗Scholar ↗

2023

Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking

Pan Wang, Liangliang Ren, Shengkai Wu, Jinrong Yang, En Yu, Hangcheng Yu +1

RA-L 2023

The point cloud based 3D single object tracking has drawn increasing attention. Although many breakthroughs have been achieved, we also reveal two severe issues. By extensive analysis, we find the prediction manner of current approaches is non-robust, i.e., exposing a misalignment gap between predic

Cited by 9 Source ↗Scholar ↗