most citedOV-PARTS: Towards Open-Vocabulary Part Segmentation

5 citations · 14 across the 7 of their papers we have counts for

collaborators

7 papers

cs.RO2024

RoboKeyGen: Robot Pose and Joint Angles Estimation via Diffusion-based 3D Keypoint Generation

Yang Tian, Jiyao Zhang, Guowei Huang +4

Estimating robot pose and joint angles is significant in advanced robotics, enabling applications like robot collaboration and online hand-eye calibration.However, the introduction…

cs.CV2024

Multi-Object Tracking by Hierarchical Visual Representations

Jinkun Cao, Jiangmiao Pang, Kris Kitani

We propose a new visual hierarchical representation paradigm for multi-object tracking. It is more effective to discriminate between objects by attending to objects' compositional…

cs.RO20244 cited

Hybrid Internal Model: Learning Agile Legged Locomotion with Simulated Robot Response

Junfeng Long, Zirui Wang, Quanyi Li +3

Robust locomotion control depends on accurate state estimations. However, the sensors of most legged robots can only provide partial and noisy observations, making the estimation p…

cs.CV20235 cited

OV-PARTS: Towards Open-Vocabulary Part Segmentation

Meng Wei, Xiaoyu Yue, Wenwei Zhang +3

Segmenting and recognizing diverse object parts is a crucial ability in applications spanning various computer vision and robotic tasks. While significant progress has been made in…

cs.CV20234 cited

DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking

Qing Lian, Tai Wang, Dahua Lin +1

Recent multi-camera 3D object detectors usually leverage temporal information to construct multi-view stereo that alleviates the ill-posed depth estimation. However, they typically…

cs.CV20231 cited

MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based Self-Supervised Pre-Training

Runsen Xu, Tai Wang, Wenwei Zhang +4

This paper introduces the Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training and a carefully designed data-efficient 3D object dete…