4 citations · 9 across the 13 of their papers we have counts for
13 papers
VectorWorld: Efficient Streaming World Model via Diffusion Flow on Vector Graphs
Chaokang Jiang, Desen Zhou, Jiuming Liu +1
Closed-loop evaluation of autonomous-driving policies requires interactive simulation beyond log replay. However, existing generative world models often degrade in closed loop due…
RegFormer++: An Efficient Large-Scale 3D LiDAR Point Registration Network with Projection-Aware 2D Transformer
Jiuming Liu, Guangming Wang, Zhe Liu +7
Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale LiDAR registration methods has been rarely explored before. Challe…
DGSLAM: 4D Dynamic Gaussian Splatting SLAM
Siting Zhu, Yuxiang Huang, Wenhua Wu +4
Recent advances in Dense Simultaneous Localization and Mapping (SLAM) have demonstrated remarkable performance in static environments. However, dense SLAM in dynamic environments r…
GMF-Drive: Gated Mamba Fusion with Spatial-Aware BEV Representation for End-to-End Autonomous Driving
Jian Wang, Chaokang Jiang, Haitao Xu
Diffusion-based models are redefining the state-of-the-art in end-to-end autonomous driving, yet their performance is increasingly hampered by a reliance on transformer-based fusio…
NeuroGauss4D-PCI: 4D Neural Fields and Gaussian Deformation Fields for Point Cloud Interpolation
Chaokang Jiang, Dalong Du, Jiuming Liu +5
Point Cloud Interpolation confronts challenges from point sparsity, complex spatiotemporal dynamics, and the difficulty of deriving complete 3D point clouds from sparse temporal in…
MAMBA4D: Efficient Long-Sequence Point Cloud Video Understanding with Disentangled Spatial-Temporal State Space Models
Jiuming Liu, Jinru Han, Lihao Liu +4
Point cloud videos can faithfully capture real-world spatial geometries and temporal dynamics, which are essential for enabling intelligent agents to understand the dynamically cha…
3DSFLabelling: Boosting 3D Scene Flow Estimation by Pseudo Auto-labelling
Chaokang Jiang, Guangming Wang, Jiuming Liu +6
Learning 3D scene flow from LiDAR point clouds presents significant difficulties, including poor generalization from synthetic datasets to real scenes, scarcity of real-world 3D la…
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Diffusion Model
Jiuming Liu, Guangming Wang, Weicai Ye +6
Scene flow estimation, which aims to predict per-point 3D displacements of dynamic scenes, is a fundamental task in the computer vision field. However, previous works commonly suff…
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…