4 citations · 7 across the 8 of their papers we have counts for
8 papers
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…
Monocular Event-Inertial Odometry with Adaptive decay-based Time Surface and Polarity-aware Tracking
Kai Tang, Xiaolei Lang, Yukai Ma +4
Event cameras have garnered considerable attention due to their advantages over traditional cameras in low power consumption, high dynamic range, and no motion blur. This paper pro…
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…