36 citations · 107 across the 29 of their papers we have counts for
30 papers
FlowHOI: Flow-based Semantics-Grounded Generation of Hand-Object Interactions for Dexterous Robot Manipulation
Huajian Zeng, Lingyun Chen, Jiaqi Yang +4
Recent vision-language-action (VLA) models can generate plausible end-effector motions, yet they often fail in long-horizon, contact-rich tasks because the underlying hand-object i…
SimGenHOI: Physically Realistic Whole-Body Humanoid-Object Interaction via Generative Modeling and Reinforcement Learning
Yuhang Lin, Yijia Xie, Jiahong Xie +5
Generating physically realistic humanoid-object interactions (HOI) is a fundamental challenge in robotics. Existing HOI generation approaches, such as diffusion-based models, often…
Gaussian-LIC2: LiDAR-Inertial-Camera Gaussian Splatting SLAM
Xiaolei Lang, Jiajun Lv, Kai Tang +5
This paper presents the first photo-realistic LiDAR-Inertial-Camera Gaussian Splatting SLAM system that simultaneously addresses visual quality, geometric accuracy, and real-time p…
L2COcc: Lightweight Camera-Centric Semantic Scene Completion via Distillation of LiDAR Model
Ruoyu Wang, Yukai Ma, Yi Yao +5
Semantic Scene Completion (SSC) constitutes a pivotal element in autonomous driving perception systems, tasked with inferring the 3D semantic occupancy of a scene from sensory data…
LiCROcc: Teach Radar for Accurate Semantic Occupancy Prediction using LiDAR and Camera
Yukai Ma, Jianbiao Mei, Xuemeng Yang +6
Semantic Scene Completion (SSC) is pivotal in autonomous driving perception, frequently confronted with the complexities of weather and illumination changes. The long-term strategy…
Gaussian-LIC: Real-Time Photo-Realistic SLAM with Gaussian Splatting and LiDAR-Inertial-Camera Fusion
Xiaolei Lang, Laijian Li, Chenming Wu +5
In this paper, we present a real-time photo-realistic SLAM method based on marrying Gaussian Splatting with LiDAR-Inertial-Camera SLAM. Most existing radiance-field-based SLAM syst…
RIDERS: Radar-Infrared Depth Estimation for Robust Sensing
Han Li, Yukai Ma, Yuehao Huang +4
Dense depth recovery is crucial in autonomous driving, serving as a foundational element for obstacle avoidance, 3D object detection, and local path planning. Adverse weather condi…
M2-CLIP: A Multimodal, Multi-task Adapting Framework for Video Action Recognition
Mengmeng Wang, Jiazheng Xing, Boyuan Jiang +6
Recently, the rise of large-scale vision-language pretrained models like CLIP, coupled with the technology of Parameter-Efficient FineTuning (PEFT), has captured substantial attrac…
RadarCam-Depth: Radar-Camera Fusion for Depth Estimation with Learned Metric Scale
Han Li, Yukai Ma, Yaqing Gu +3
We present a novel approach for metric dense depth estimation based on the fusion of a single-view image and a sparse, noisy Radar point cloud. The direct fusion of heterogeneous R…
Coco-LIC: Continuous-Time Tightly-Coupled LiDAR-Inertial-Camera Odometry using Non-Uniform B-spline
Xiaolei Lang, Chao Chen, Kai Tang +4
In this paper, we propose an efficient continuous-time LiDAR-Inertial-Camera Odometry, utilizing non-uniform B-splines to tightly couple measurements from the LiDAR, IMU, and camer…
SimpleMapping: Real-Time Visual-Inertial Dense Mapping with Deep Multi-View Stereo
Yingye Xin, Xingxing Zuo, Dongyue Lu +1
We present a real-time visual-inertial dense mapping method capable of performing incremental 3D mesh reconstruction with high quality using only sequential monocular images and in…
Incremental Dense Reconstruction from Monocular Video with Guided Sparse Feature Volume Fusion
Xingxing Zuo, Nan Yang, Nathaniel Merrill +2
Incrementally recovering 3D dense structures from monocular videos is of paramount importance since it enables various robotics and AR applications. Feature volumes have recently b…
Correlation Pyramid Network for 3D Single Object Tracking
Mengmeng Wang, Teli Ma, Xingxing Zuo +2
3D LiDAR-based single object tracking (SOT) has gained increasing attention as it plays a crucial role in 3D applications such as autonomous driving. The central problem is how to…
Continuous-Time Fixed-Lag Smoothing for LiDAR-Inertial-Camera SLAM
Jiajun Lv, Xiaolei Lang, Jinhong Xu +3
Localization and mapping with heterogeneous multi-sensor fusion have been prevalent in recent years. To adequately fuse multi-modal sensor measurements received at different time i…
High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF
Lu Sang, Bjoern Haefner, Xingxing Zuo +1
Fine-detailed reconstructions are in high demand in many applications. However, most of the existing RGB-D reconstruction methods rely on pre-calculated accurate camera poses to re…
Ctrl-VIO: Continuous-Time Visual-Inertial Odometry for Rolling Shutter Cameras
Xiaolei Lang, Jiajun Lv, Jianxin Huang +3
In this paper, we propose a probabilistic continuous-time visual-inertial odometry (VIO) for rolling shutter cameras. The continuous-time trajectory formulation naturally facilitat…
Visual-Inertial SLAM with Tightly-Coupled Dropout-Tolerant GPS Fusion
Simon Boche, Xingxing Zuo, Simon Schaefer +1
Robotic applications are continuously striving towards higher levels of autonomy. To achieve that goal, a highly robust and accurate state estimation is indispensable. Combining vi…
Observability-Aware Intrinsic and Extrinsic Calibration of LiDAR-IMU Systems
Jiajun Lv, Xingxing Zuo, Kewei Hu +3
Accurate and reliable sensor calibration is essential to fuse LiDAR and inertial measurements, which are usually available in robotic applications. In this paper, we propose a nove…
Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation
Nathaniel Merrill, Yuliang Guo, Xingxing Zuo +5
We propose a keypoint-based object-level SLAM framework that can provide globally consistent 6DoF pose estimates for symmetric and asymmetric objects alike. To the best of our know…
Online Self-Calibration for Visual-Inertial Navigation Systems: Models, Analysis and Degeneracy
Yulin Yang, Patrick Geneva, Xingxing Zuo +1
In this paper, we study in-depth the problem of online self-calibration for robust and accurate visual-inertial state estimation. In particular, we first perform a complete observa…
CLINS: Continuous-Time Trajectory Estimation for LiDAR-Inertial System
Jiajun Lv, Kewei Hu, Jinhong Xu +3
In this paper, we propose a highly accurate continuous-time trajectory estimation framework dedicated to SLAM (Simultaneous Localization and Mapping) applications, which enables fu…
MBA-VO: Motion Blur Aware Visual Odometry
Peidong Liu, Xingxing Zuo, Viktor Larsson +1
Motion blur is one of the major challenges remaining for visual odometry methods. In low-light conditions where longer exposure times are necessary, motion blur can appear even for…
CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth
Xingxing Zuo, Nathaniel Merrill, Wei Li +3
In this work, we present a lightweight, tightly-coupled deep depth network and visual-inertial odometry (VIO) system, which can provide accurate state estimates and dense depth map…
LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking
Xingxing Zuo, Yulin Yang, Patrick Geneva +4
Multi-sensor fusion of multi-modal measurements from commodity inertial, visual and LiDAR sensors to provide robust and accurate 6DOF pose estimation holds great potential in robot…