activity
20182024
most citedAB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics

45 citations · 70 across the 10 of their papers we have counts for

collaborators

13 papers

cs.RO20222 cited

Curiosity Driven Self-supervised Tactile Exploration of Unknown Objects

Yujie Lu, Jianren Wang, Vikash Kumar

Intricate behaviors an organism can exhibit is predicated on its ability to sense and effectively interpret complexities of its surroundings. Relevant information is often distribu…

cs.CV2021

Wanderlust: Online Continual Object Detection in the Real World

Jianren Wang, Xin Wang, Yue Shang-Guan +1

Online continual learning from data streams in dynamic environments is a critical direction in the computer vision field. However, realistic benchmarks and fundamental studies in t…

cs.CV20202 cited

PanoNet3D: Combining Semantic and Geometric Understanding for LiDARPoint Cloud Detection

Xia Chen, Jianren Wang, David Held +1

Visual data in autonomous driving perception, such as camera image and LiDAR point cloud, can be interpreted as a mixture of two aspects: semantic feature and geometric structure.…

cs.RO2020

CLOUD: Contrastive Learning of Unsupervised Dynamics

Jianren Wang, Yujie Lu, Hang Zhao

Developing agents that can perform complex control tasks from high dimensional observations such as pixels is challenging due to difficulties in learning dynamics efficiently. In t…

cs.CV2020

Uncertainty-aware Self-supervised 3D Data Association

Jianren Wang, Siddharth Ancha, Yi-Ting Chen +1

3D object trackers usually require training on large amounts of annotated data that is expensive and time-consuming to collect. Instead, we propose leveraging vast unlabeled datase…

cs.CV202045 cited

AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics

Xinshuo Weng, Jianren Wang, David Held +1

3D multi-object tracking (MOT) is essential to applications such as autonomous driving. Recent work focuses on developing accurate systems giving less attention to computational co…