1 citations · 1 across the 10 of their papers we have counts for
14 papers
4D Radar Perception Algorithms for Autonomous Driving: A Review
Xumin Wu, Jun Zhou, Jilin Mei +2
Research on 4D millimeter-wave radar perception algorithms has flourished in recent years, extending from signal processing and object detection to semantic segmentation, motion es…
Physical-State-Guided Diffusion Sampling for Full-Waveform Inversion
Chen Min, Haowen Jiang, Zheng Ma +1
Full waveform inversion (FWI) estimates subsurface velocity from seismic recordings, but its ill-posedness and nonlinearity make accurate reconstruction strongly dependent on initi…
Decoupled Latent Optimization of Diffusion Models for Full Waveform Inversion
Chen Min, Zheng Ma
Full waveform inversion (FWI) recovers subsurface velocity from seismic recordings by solving a severely ill-posed, nonconvex PDE-constrained optimization. Classical regularizers s…
GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment
Ziang Guo, Chen Min, Xuefeng Zhang +5
End-to-end (E2E) autonomous driving aims to directly map sensory observations to driving actions, but its real-world deployment is hindered by evolving data distributions and the h…
Ground4D: Spatially-Grounded Feedforward 4D Reconstruction for Unstructured Off-Road Scenes
Shuo Wang, Jilin Mei, Fuyang Liu +6
Feedforward Gaussian Splatting has recently emerged as an efficient paradigm for 4D reconstruction in autonomous driving. However, in unstructured off-road scenes, its performance…
Towards All-Day Perception for Off-Road Driving: A Large-Scale Multispectral Dataset and Comprehensive Benchmark
Shuo Wang, Jilin Mei, Wenfei Guan +4
Off-road nighttime autonomous driving suffers from unreliable visible-light perception, making infrared modality crucial for accurate freespace detection. However, progress remains…
SSI-DM: Singularity Skipping Inversion of Diffusion Models
Chen Min, Enze Jiang, Jishen Peng +1
Inverting real images into the noise space is essential for editing tasks using diffusion models, yet existing methods produce non-Gaussian noise with poor editability due to the i…
OT-Drive: Out-of-Distribution Off-Road Traversable Area Segmentation via Optimal Transport
Zhihua Zhao, Guoqiang Li, Chen Min +1
Reliable traversable area segmentation in unstructured environments is critical for planning and decision-making in autonomous driving. However, existing data-driven approaches oft…
Beyond Endpoints: Path-Centric Reasoning for Vectorized Off-Road Network Extraction
Wenfei Guan, Jilin Mei, Tong Shen +4
Deep learning has advanced vectorized road extraction in urban settings, yet off-road environments remain underexplored and challenging. A significant domain gap causes advanced mo…
Advancing Off-Road Autonomous Driving: The Large-Scale ORAD-3D Dataset and Comprehensive Benchmarks
Chen Min, Jilin Mei, Heng Zhai +12
A major bottleneck in off-road autonomous driving research lies in the scarcity of large-scale, high-quality datasets and benchmarks. To bridge this gap, we present ORAD-3D, which,…
UNIV: Unified Foundation Model for Infrared and Visible Modalities
Fangyuan Mao, Shuo Wang, Jilin Mei +6
Joint RGB-infrared perception is essential for achieving robustness under diverse weather and illumination conditions. Although foundation models excel within single modalities, th…
CORENet: Cross-Modal 4D Radar Denoising Network with LiDAR Supervision for Autonomous Driving
Fuyang Liu, Jilin Mei, Fangyuan Mao +3
4D radar-based object detection has garnered great attention for its robustness in adverse weather conditions and capacity to deliver rich spatial information across diverse drivin…
WildOcc: A Benchmark for Off-Road 3D Semantic Occupancy Prediction
Heng Zhai, Jilin Mei, Chen Min +3
3D semantic occupancy prediction is an essential part of autonomous driving, focusing on capturing the geometric details of scenes. Off-road environments are rich in geometric info…
Adaptive Learning for Multi-view Stereo Reconstruction
Qinglu Min, Jie Zhao, Zhihao Zhang +1
Deep learning has recently demonstrated its excellent performance on the task of multi-view stereo (MVS). However, loss functions applied for deep MVS are rarely studied. In this p…