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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…
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…
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…
ROD: RGB-Only Fast and Efficient Off-road Freespace Detection
Tong Sun, Hongliang Ye, Jilin Mei +4
Off-road freespace detection is more challenging than on-road scenarios because of the blurred boundaries of traversable areas. Previous state-of-the-art (SOTA) methods employ mult…
MASTER: Multimodal Segmentation with Text Prompts
Fuyang Liu, Shun Lu, Jilin Mei +1
RGB-Thermal fusion is a potential solution for various weather and light conditions in challenging scenarios. However, plenty of studies focus on designing complex modules to fuse…
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…