15 citations · 27 across the 17 of their papers we have counts for
21 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…
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…
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…
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…
From Flatland to Space: Teaching Vision-Language Models to Perceive and Reason in 3D
Jiahui Zhang, Yurui Chen, Yanpeng Zhou +10
Recent advances in LVLMs have improved vision-language understanding, but they still struggle with spatial perception, limiting their ability to reason about complex 3D scenes. Unl…
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…
Autonomous Driving in Unstructured Environments: How Far Have We Come?
Chen Min, Shubin Si, Xu Wang +14
Research on autonomous driving in unstructured outdoor environments is less advanced than in structured urban settings due to challenges like environmental diversities and scene co…
Proto-OOD: Enhancing OOD Object Detection with Prototype Feature Similarity
Junkun Chen, Jilin Mei, Liang Chen +3
Neural networks that are trained on limited category samples often mispredict out-of-distribution (OOD) objects. We observe that features of the same category are more tightly clus…
TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation
Junbao Zhou, Jilin Mei, Pengze Wu +4
In autonomous driving, 3D LiDAR plays a crucial role in understanding the vehicle's surroundings. However, the newly emerged, unannotated objects presents few-shot learning problem…
PID: Physics-Informed Diffusion Model for Infrared Image Generation
Fangyuan Mao, Jilin Mei, Shun Lu +4
Infrared imaging technology has gained significant attention for its reliable sensing ability in low visibility conditions, prompting many studies to convert the abundant RGB image…
Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving
Jilin Mei, Junbao Zhou, Yu Hu
In autonomous driving, the novel objects and lack of annotations challenge the traditional 3D LiDAR semantic segmentation based on deep learning. Few-shot learning is a feasible wa…
PA&DA: Jointly Sampling PAth and DAta for Consistent NAS
Shun Lu, Yu Hu, Longxing Yang +4
Based on the weight-sharing mechanism, one-shot NAS methods train a supernet and then inherit the pre-trained weights to evaluate sub-models, largely reducing the search cost. Howe…
Scene Context Based Semantic Segmentation for 3D LiDAR Data in Dynamic Scene
Jilin Mei, Huijing Zhao
We propose a graph neural network(GNN) based method to incorporate scene context for the semantic segmentation of 3D LiDAR data. The problem is defined as building a graph to repre…
SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances
Yancheng Pan, Biao Gao, Jilin Mei +3
3D semantic segmentation is one of the key tasks for autonomous driving system. Recently, deep learning models for 3D semantic segmentation task have been widely researched, but th…
Incorporating Human Domain Knowledge in 3D LiDAR-based Semantic Segmentation
Jilin Mei, Huijing Zhao
This work studies semantic segmentation using 3D LiDAR data. Popular deep learning methods applied for this task require a large number of manual annotations to train the parameter…
Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using Semi-supervised Learning
Jilin Mei, Biao Gao, Donghao Xu +3
This work studies the semantic segmentation of 3D LiDAR data in dynamic scenes for autonomous driving applications. A system of semantic segmentation using 3D LiDAR data, including…