Modeling Continuous Motion for 3D Point Cloud Object Tracking

no code implementations • 14 Mar 2023 • Zhipeng Luo, Gongjie Zhang, Changqing Zhou, Zhonghua Wu, Qingyi Tao, Lewei Lu, Shijian Lu

The task of 3D single object tracking (SOT) with LiDAR point clouds is crucial for various applications, such as autonomous driving and robotics.

3D Single Object Tracking Autonomous Driving +2

DETR4D: Direct Multi-View 3D Object Detection with Sparse Attention

no code implementations • 15 Dec 2022 • Zhipeng Luo, Changqing Zhou, Gongjie Zhang, Shijian Lu

3D object detection with surround-view images is an essential task for autonomous driving.

3D Object Detection Autonomous Driving +2

Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking with Transformer

1 code implementation • 10 Aug 2022 • Zhipeng Luo, Changqing Zhou, Liang Pan, Gongjie Zhang, Tianrui Liu, Yueru Luo, Haiyu Zhao, Ziwei Liu, Shijian Lu

In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in consecutive frames given an object template.

3D Object Tracking Autonomous Driving +3

TransPillars: Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection

no code implementations • 4 Aug 2022 • Zhipeng Luo, Gongjie Zhang, Changqing Zhou, Tianrui Liu, Shijian Lu, Liang Pan

3D object detection using point clouds has attracted increasing attention due to its wide applications in autonomous driving and robotics.

3D Object Detection Autonomous Driving +3

PTTR: Relational 3D Point Cloud Object Tracking with Transformer

1 code implementation • CVPR 2022 • Changqing Zhou, Zhipeng Luo, Yueru Luo, Tianrui Liu, Liang Pan, Zhongang Cai, Haiyu Zhao, Shijian Lu

In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in the current search point cloud given a template point cloud.

3D Object Tracking Object +3

Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency

1 code implementation • ICCV 2021 • Zhipeng Luo, Zhongang Cai, Changqing Zhou, Gongjie Zhang, Haiyu Zhao, Shuai Yi, Shijian Lu, Hongsheng Li, Shanghang Zhang, Ziwei Liu

In addition, existing 3D domain adaptive detection methods often assume prior access to the target domain annotations, which is rarely feasible in the real world.

3D Object Detection Autonomous Driving +1

Sparta: Spatially Attentive and Adversarially Robust Activation

no code implementations • 18 May 2021 • Qing Guo, Felix Juefei-Xu, Changqing Zhou, Wei Feng, Yang Liu, Song Wang

In both cases, Sparta leads to CNNs with higher robustness than the vanilla ReLU, verifying the flexibility and versatility of the proposed method.

Auto-Exposure Fusion for Single-Image Shadow Removal

2 code implementations • CVPR 2021 • Lan Fu, Changqing Zhou, Qing Guo, Felix Juefei-Xu, Hongkai Yu, Wei Feng, Yang Liu, Song Wang

We conduct extensive experiments on the ISTD, ISTD+, and SRD datasets to validate our method's effectiveness and show better performance in shadow regions and comparable performance in non-shadow regions over the state-of-the-art methods.

Image Shadow Removal Shadow Removal

Sparta: Spatially Attentive and Adversarially Robust Activations

no code implementations • 1 Jan 2021 • Qing Guo, Felix Juefei-Xu, Changqing Zhou, Lei Ma, Xiaofei Xie, Wei Feng, Yang Liu

Moreover, comprehensive evaluations have demonstrated two important properties of our method: First, superior transferability across DNNs.

Denoising