21 citations · 29 across the 9 of their papers we have counts for
12 papers · 1 filter
UniUGG: Unified 3D Understanding and Generation via Geometric-Semantic Encoding
Yueming Xu, Jiahui Zhang, Ze Huang +12
Despite the impressive progress on understanding and generating images shown by the recent unified architectures, the integration of 3D tasks remains challenging and largely unexpl…
4D-VLA: Spatiotemporal Vision-Language-Action Pretraining with Cross-Scene Calibration
Jiahui Zhang, Yurui Chen, Yueming Xu +8
Leveraging diverse robotic data for pretraining remains a critical challenge. Existing methods typically model the dataset's action distribution using simple observations as inputs…
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…
LaneCorrect: Self-supervised Lane Detection
Ming Nie, Xinyue Cai, Hang Xu +1
Lane detection has evolved highly functional autonomous driving system to understand driving scenes even under complex environments. In this paper, we work towards developing a gen…
LaneGraph2Seq: Lane Topology Extraction with Language Model via Vertex-Edge Encoding and Connectivity Enhancement
Renyuan Peng, Xinyue Cai, Hang Xu +4
Understanding road structures is crucial for autonomous driving. Intricate road structures are often depicted using lane graphs, which include centerline curves and connections for…
Translating Images to Road Network: A Sequence-to-Sequence Perspective
Jiachen Lu, Ming Nie, Bozhou Zhang +6
The extraction of road network is essential for the generation of high-definition maps since it enables the precise localization of road landmarks and their interconnections. Howev…
Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Driving
Ming Nie, Renyuan Peng, Chunwei Wang +4
Large vision-language models (VLMs) have garnered increasing interest in autonomous driving areas, due to their advanced capabilities in complex reasoning tasks essential for highl…
RCLane: Relay Chain Prediction for Lane Detection
Shenghua Xu, Xinyue Cai, Bin Zhao +4
Lane detection is an important component of many real-world autonomous systems. Despite a wide variety of lane detection approaches have been proposed, reporting steady benchmark i…
ONCE-3DLanes: Building Monocular 3D Lane Detection
Fan Yan, Ming Nie, Xinyue Cai +7
We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space. Conventional 2D lane detection from a monocular image yields poor performa…
Laneformer: Object-aware Row-Column Transformers for Lane Detection
Jianhua Han, Xiajun Deng, Xinyue Cai +4
We present Laneformer, a conceptually simple yet powerful transformer-based architecture tailored for lane detection that is a long-standing research topic for visual perception in…
VEGA: Towards an End-to-End Configurable AutoML Pipeline
Bochao Wang, Hang Xu, Jiajin Zhang +21
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models. However, designing an integrated Auto…
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
Hang Xu, Shaoju Wang, Xinyue Cai +3
We address the curve lane detection problem which poses more realistic challenges than conventional lane detection for better facilitating modern assisted/autonomous driving system…