7 citations · 24 across the 12 of their papers we have counts for
12 papers
EADReg: Probabilistic Correspondence Generation with Efficient Autoregressive Diffusion Model for Outdoor Point Cloud Registration
Linrui Gong, Jiuming Liu, Junyi Ma +3
Diffusion models have shown the great potential in the point cloud registration (PCR) task, especially for enhancing the robustness to challenging cases. However, existing diffusio…
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…
Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy
Jiuming Liu, Ruiji Yu, Yian Wang +4
Recently, state space model (SSM) has gained great attention due to its promising performance, linear complexity, and long sequence modeling ability in both language and image doma…
DVLO: Deep Visual-LiDAR Odometry with Local-to-Global Feature Fusion and Bi-Directional Structure Alignment
Jiuming Liu, Dong Zhuo, Zhiheng Feng +4
Information inside visual and LiDAR data is well complementary derived from the fine-grained texture of images and massive geometric information in point clouds. However, it remain…
SemGauss-SLAM: Dense Semantic Gaussian Splatting SLAM
Siting Zhu, Renjie Qin, Guangming Wang +2
We propose SemGauss-SLAM, a dense semantic SLAM system utilizing 3D Gaussian representation, that enables accurate 3D semantic mapping, robust camera tracking, and high-quality ren…
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…
Spherical Frustum Sparse Convolution Network for LiDAR Point Cloud Semantic Segmentation
Yu Zheng, Guangming Wang, Jiuming Liu +2
LiDAR point cloud semantic segmentation enables the robots to obtain fine-grained semantic information of the surrounding environment. Recently, many works project the point cloud…
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…
SNI-SLAM: Semantic Neural Implicit SLAM
Siting Zhu, Guangming Wang, Hermann Blum +4
We propose SNI-SLAM, a semantic SLAM system utilizing neural implicit representation, that simultaneously performs accurate semantic mapping, high-quality surface reconstruction, a…
SC-NeRF: Self-Correcting Neural Radiance Field with Sparse Views
Liang Song, Guangming Wang, Jiuming Liu +3
In recent studies, the generalization of neural radiance fields for novel view synthesis task has been widely explored. However, existing methods are limited to objects and indoor…
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…