4 citations · 6 across the 15 of their papers we have counts for
14 papers · 1 filter
SFVO: Decoupled Confidence-Guided Stereo-Flow Visual Odometry with Bidirectional PnP
Kai Zhang, Guoyang Zhao, Jun Ma
Deep learning-based visual odometry (VO) has achieved significant progress, yet most existing methods focus on a monocular approach, which suffers from scale ambiguity. Stereo VO p…
MacTok: Robust Continuous Tokenization for Image Generation
Hengyu Zeng, Xin Gao, Guanghao Li +5
Continuous image tokenizers enable efficient visual generation, and those based on variational frameworks can learn smooth, structured latent representations through KL regularizat…
Annotation-Free Detection of Drivable Areas and Curbs Leveraging LiDAR Point Cloud Maps
Fulong Ma, Daojie Peng, Jun Ma
Drivable areas and curbs are critical traffic elements for autonomous driving, forming essential components of the vehicle visual perception system and ensuring driving safety. Dee…
Traffic Sign Recognition in Autonomous Driving: Dataset, Benchmark, and Field Experiment
Guoyang Zhao, Weiqing Qi, Kai Zhang +7
Traffic Sign Recognition (TSR) is a core perception capability for autonomous driving, where robustness to cross-region variation, long-tailed categories, and semantic ambiguity is…
Structured Observation Language for Efficient and Generalizable Vision-Language Navigation
Daojie Peng, Fulong Ma, Jun Ma
Vision-Language Navigation (VLN) requires an embodied agent to navigate complex environments by following natural language instructions, which typically demands tight fusion of vis…
Task-Oriented Pre-Training for Drivable Area Detection
Fulong Ma, Guoyang Zhao, Weiqing Qi +2
Pre-training techniques play a crucial role in deep learning, enhancing models' performance across a variety of tasks. By initially training on large datasets and subsequently fine…
Annotation-Free Curb Detection Leveraging Altitude Difference Image
Fulong Ma, Peng Hou, Yuxuan Liu +3
Road curbs are considered as one of the crucial and ubiquitous traffic features, which are essential for ensuring the safety of autonomous vehicles. Current methods for detecting c…
Erase, then Redraw: A Novel Data Augmentation Approach for Free Space Detection Using Diffusion Model
Fulong Ma, Weiqing Qi, Guoyang Zhao +2
Data augmentation is one of the most common tools in deep learning, underpinning many recent advances including tasks such as classification, detection, and semantic segmentation.…
TSCLIP: Robust CLIP Fine-Tuning for Worldwide Cross-Regional Traffic Sign Recognition
Guoyang Zhao, Fulong Ma, Weiqing Qi +4
Traffic sign is a critical map feature for navigation and traffic control. Nevertheless, current methods for traffic sign recognition rely on traditional deep learning models, whic…
FisheyeDepth: A Real Scale Self-Supervised Depth Estimation Model for Fisheye Camera
Guoyang Zhao, Yuxuan Liu, Weiqing Qi +3
Accurate depth estimation is crucial for 3D scene comprehension in robotics and autonomous vehicles. Fisheye cameras, known for their wide field of view, have inherent geometric be…
CLRKDNet: Speeding up Lane Detection with Knowledge Distillation
Weiqing Qi, Guoyang Zhao, Fulong Ma +2
Road lanes are integral components of the visual perception systems in intelligent vehicles, playing a pivotal role in safe navigation. In lane detection tasks, balancing accuracy…
Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
Fulong Ma, Weiqing Qi, Guoyang Zhao +5
3D lane detection is essential in autonomous driving as it extracts structural and traffic information from the road in three-dimensional space, aiding self-driving cars in logical…
CurbNet: Curb Detection Framework Based on LiDAR Point Cloud Segmentation
Guoyang Zhao, Fulong Ma, Weiqing Qi +3
Curb detection is a crucial function in intelligent driving, essential for determining drivable areas on the road. However, the complexity of road environments makes curb detection…
Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training
Fulong Ma, Xiaoyang Yan, Guoyang Zhao +4
Monocular 3D object detection plays a crucial role in autonomous driving. However, existing monocular 3D detection algorithms depend on 3D labels derived from LiDAR measurements, w…