74 citations · 181 across the 19 of their papers we have counts for
19 papers
RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation
Chang Nie, Guangming Wang, Zhe Liu +3
Robust estimation is a crucial and still challenging task, which involves estimating model parameters in noisy environments. Although conventional sampling consensus-based algorith…
DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds
Chensheng Peng, Guangming Wang, Xian Wan Lo +5
Point clouds are naturally sparse, while image pixels are dense. The inconsistency limits feature fusion from both modalities for point-wise scene flow estimation. Previous methods…
End-to-end 2D-3D Registration between Image and LiDAR Point Cloud for Vehicle Localization
Guangming Wang, Yu Zheng, Yuxuan Wu +5
Robot localization using a built map is essential for a variety of tasks including accurate navigation and mobile manipulation. A popular approach to robot localization is based on…
Efficient Robot Skill Learning with Imitation from a Single Video for Contact-Rich Fabric Manipulation
Shengzeng Huo, Anqing Duan, Lijun Han +3
Classical policy search algorithms for robotics typically require performing extensive explorations, which are time-consuming and expensive to implement with real physical platform…
RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration
Jiuming Liu, Guangming Wang, Zhe Liu +3
Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale registration methods are rarely explored. Challenges mainly arise…
3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion
Chaokang Jiang, Guangming Wang, Yanzi Miao +1
3D scene flow characterizes how the points at the current time flow to the next time in the 3D Euclidean space, which possesses the capacity to infer autonomously the non-rigid mot…
FFPA-Net: Efficient Feature Fusion with Projection Awareness for 3D Object Detection
Chaokang Jiang, Guangming Wang, Jinxing Wu +2
Promising complementarity exists between the texture features of color images and the geometric information of LiDAR point clouds. However, there still present many challenges for…
Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance
Guangming Wang, Zhiheng Feng, Chaokang Jiang +1
Scene flow represents the 3D motion of each point in the scene, which explicitly describes the distance and the direction of each point's movement. Scene flow estimation is used in…
Pseudo-LiDAR for Visual Odometry
Huiying Deng, Guangming Wang, Zhiheng Feng +4
In the existing methods, LiDAR odometry shows superior performance, but visual odometry is still widely used for its price advantage. Conventionally, the task of visual odometry ma…
What Matters for 3D Scene Flow Network
Guangming Wang, Yunzhe Hu, Zhe Liu +4
3D scene flow estimation from point clouds is a low-level 3D motion perception task in computer vision. Flow embedding is a commonly used technique in scene flow estimation, and it…
Unsupervised Learning of 3D Scene Flow from Monocular Camera
Guangming Wang, Xiaoyu Tian, Ruiqi Ding +1
Scene flow represents the motion of points in the 3D space, which is the counterpart of the optical flow that represents the motion of pixels in the 2D image. However, it is diffic…
Interactive Multi-scale Fusion of 2D and 3D Features for Multi-object Tracking
Guangming Wang, Chensheng Peng, Jinpeng Zhang +1
Multiple object tracking (MOT) is a significant task in achieving autonomous driving. Traditional works attempt to complete this task, either based on point clouds (PC) collected b…
DetFlowTrack: 3D Multi-object Tracking based on Simultaneous Optimization of Object Detection and Scene Flow Estimation
Yueling Shen, Guangming Wang, Hesheng Wang
3D Multi-Object Tracking (MOT) is an important part of the unmanned vehicle perception module. Most methods optimize object detection and data association independently. These meth…
Residual 3D Scene Flow Learning with Context-Aware Feature Extraction
Guangming Wang, Yunzhe Hu, Xinrui Wu +1
Scene flow estimation is the task to predict the point-wise or pixel-wise 3D displacement vector between two consecutive frames of point clouds or images, which has important appli…
NccFlow: Unsupervised Learning of Optical Flow With Non-occlusion from Geometry
Guangming Wang, Shuaiqi Ren, Hesheng Wang
Optical flow estimation is a fundamental problem of computer vision and has many applications in the fields of robot learning and autonomous driving. This paper reveals novel geome…
PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization
Guangming Wang, Xinrui Wu, Zhe Liu +1
A novel 3D point cloud learning model for deep LiDAR odometry, named PWCLO-Net, using hierarchical embedding mask optimization is proposed in this paper. In this model, the Pyramid…
SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments
Hanjiang Hu, Baoquan Yang, Zhijian Qiao +6
Different environments pose a great challenge to the outdoor robust visual perception for long-term autonomous driving, and the generalization of learning-based algorithms on diffe…
Hierarchical Attention Learning of Scene Flow in 3D Point Clouds
Guangming Wang, Xinrui Wu, Zhe Liu +1
Scene flow represents the 3D motion of every point in the dynamic environments. Like the optical flow that represents the motion of pixels in 2D images, 3D motion representation of…
Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D Geometry
Guangming Wang, Chi Zhang, Hesheng Wang +3
In autonomous driving, monocular sequences contain lots of information. Monocular depth estimation, camera ego-motion estimation and optical flow estimation in consecutive frames a…