22 citations · 35 across the 21 of their papers we have counts for
22 papers
Bridging Scene Generation and Planning: Driving with World Model via Unifying Vision and Motion Representation
Xingtai Gui, Meijie Zhang, Tianyi Yan +5
End-to-end autonomous driving aims to generate safe and plausible planning policies from raw sensor input. Driving world models have shown great potential in learning rich represen…
TrajDiff: End-to-end Autonomous Driving without Perception Annotation
Xingtai Gui, Jianbo Zhao, Wencheng Han +5
End-to-end autonomous driving systems directly generate driving policies from raw sensor inputs. While these systems can extract effective environmental features for planning, rely…
HiCoGen: Hierarchical Compositional Text-to-Image Generation in Diffusion Models via Reinforcement Learning
Hongji Yang, Yucheng Zhou, Wencheng Han +4
Recent advances in diffusion models have demonstrated impressive capability in generating high-quality images for simple prompts. However, when confronted with complex prompts invo…
AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models
Tianyi Yan, Tao Tang, Xingtai Gui +11
End-to-end models for autonomous driving hold the promise of learning complex behaviors directly from sensor data, but face critical challenges in safety and handling long-tail eve…
RLGF: Reinforcement Learning with Geometric Feedback for Autonomous Driving Video Generation
Tianyi Yan, Wencheng Han, Xia Zhou +4
Synthetic data is crucial for advancing autonomous driving (AD) systems, yet current state-of-the-art video generation models, despite their visual realism, suffer from subtle geom…
Reducing CT Metal Artifacts by Learning Latent Space Alignment with Gemstone Spectral Imaging Data
Wencheng Han, Dongqian Guo, Xiao Chen +3
Metal artifacts in CT slices have long posed challenges in medical diagnostics. These artifacts degrade image quality, resulting in suboptimal visualization and complicating the ac…
OLiDM: Object-aware LiDAR Diffusion Models for Autonomous Driving
Tianyi Yan, Junbo Yin, Xianpeng Lang +3
To enhance autonomous driving safety in complex scenarios, various methods have been proposed to simulate LiDAR point cloud data. Nevertheless, these methods often face challenges…
RAWMamba: Unified sRGB-to-RAW De-rendering With State Space Model
Hongjun Chen, Wencheng Han, Huan Zheng +1
Recent advancements in sRGB-to-RAW de-rendering have increasingly emphasized metadata-driven approaches to reconstruct RAW data from sRGB images, supplemented by partial RAW inform…
DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulation
Tianyi Yan, Dongming Wu, Wencheng Han +5
Autonomous driving evaluation requires simulation environments that closely replicate actual road conditions, including real-world sensory data and responsive feedback loops. Howev…
Towards High-Fidelity 3D Portrait Generation with Rich Details by Cross-View Prior-Aware Diffusion
Haoran Wei, Wencheng Han, Xingping Dong +1
Recent diffusion-based Single-image 3D portrait generation methods typically employ 2D diffusion models to provide multi-view knowledge, which is then distilled into 3D representat…
ALOcc: Adaptive Lifting-Based 3D Semantic Occupancy and Cost Volume-Based Flow Predictions
Dubing Chen, Jin Fang, Wencheng Han +5
3D semantic occupancy and flow prediction are fundamental to spatiotemporal scene understanding. This paper proposes a vision-based framework with three targeted improvements. Firs…
Decoupling Fine Detail and Global Geometry for Compressed Depth Map Super-Resolution
Huan Zheng, Wencheng Han, Jianbing Shen
Recovering high-quality depth maps from compressed sources has gained significant attention due to the limitations of consumer-grade depth cameras and the bandwidth restrictions du…
High-Precision Self-Supervised Monocular Depth Estimation with Rich-Resource Prior
Wencheng Han, Jianbing Shen
In the area of self-supervised monocular depth estimation, models that utilize rich-resource inputs, such as high-resolution and multi-frame inputs, typically achieve better perfor…
RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception
Chunliang Li, Wencheng Han, Junbo Yin +2
Concurrent processing of multiple autonomous driving 3D perception tasks within the same spatiotemporal scene poses a significant challenge, in particular due to the computational…
AdaOcc: Adaptive Forward View Transformation and Flow Modeling for 3D Occupancy and Flow Prediction
Dubing Chen, Wencheng Han, Jin Fang +1
In this technical report, we present our solution for the Vision-Centric 3D Occupancy and Flow Prediction track in the nuScenes Open-Occ Dataset Challenge at CVPR 2024. Our innovat…
IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection
Junbo Yin, Jianbing Shen, Runnan Chen +4
Bird's eye view (BEV) representation has emerged as a dominant solution for describing 3D space in autonomous driving scenarios. However, objects in the BEV representation typicall…
DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving
Wencheng Han, Dongqian Guo, Cheng-Zhong Xu +1
In the field of autonomous driving, two important features of autonomous driving car systems are the explainability of decision logic and the accuracy of environmental perception.…
DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object Detection
Li Xiang, Junbo Yin, Wei Li +3
Vehicle-to-Everything (V2X) collaborative perception has recently gained significant attention due to its capability to enhance scene understanding by integrating information from…
TopoMLP: A Simple yet Strong Pipeline for Driving Topology Reasoning
Dongming Wu, Jiahao Chang, Fan Jia +3
Topology reasoning aims to comprehensively understand road scenes and present drivable routes in autonomous driving. It requires detecting road centerlines (lane) and traffic eleme…
Decoupling the Curve Modeling and Pavement Regression for Lane Detection
Wencheng Han, Jianbing Shen
The curve-based lane representation is a popular approach in many lane detection methods, as it allows for the representation of lanes as a whole object and maximizes the use of ho…
Language Prompt for Autonomous Driving
Dongming Wu, Wencheng Han, Yingfei Liu +4
A new trend in the computer vision community is to capture objects of interest following flexible human command represented by a natural language prompt. However, the progress of u…
Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution Network
Wencheng Han, Junbo Yin, Jianbing Shen
Monocular depth estimation is known as an ill-posed task in which objects in a 2D image usually do not contain sufficient information to predict their depth. Thus, it acts differen…