1 citations · 2 across the 19 of their papers we have counts for
23 papers
Pondering the Way: Spatial-perceiving World Action Model for Embodied Navigation
Hong Chen, Daqi Liu, Zehan Zhang +10
Existing world model-based planners for visual navigation typically follow a verification-centric paradigm, decoupling goal intent from trajectory synthesis. This approach suffers…
DriveReward: A Comprehensive Dataset and Generative Vision-Language Reward Model for Autonomous Driving
Qimao Chen, Fang Li, Yuechen Luo +11
Reward models play a pivotal role in reinforcement learning (RL) and multi-modal trajectory selection for autonomous driving. However, acquiring such rewards typically relies on ha…
AutoMine Solution for AV2 2026 Scenario Mining Challenge
Songliang Cao, Jiele Zhao, Yuru Wang +10
With the development of autonomous driving systems, mining high-value, safety-critical, and planning-relevant scenarios from large-scale driving logs has become essential for data-…
ReWorld: Representation Learning for World Action Models
Tianze Xia, Lijun Zhou, Kaixin Xiong +8
World Action Models (WAMs) unify future environment prediction with action generation for autonomous driving, yet existing approaches optimize only the final outputs, leaving inter…
PointForward: Feedforward Driving Reconstruction through Point-Aligned Representations
Cheng Chi, Xianqi Wang, Hongcheng Luo +9
High-fidelity reconstruction of driving scenes is crucial for autonomous driving. While recent feedforward 3D Gaussian Splatting (3DGS) methods enable fast reconstruction, their pe…
Toward Physically Consistent Driving Video World Models under Challenging Trajectories
Jiawei Zhou, Zhenxin Zhu, Lingyi Du +10
Video generation models have shown strong potential as world models for autonomous driving simulation. However, existing approaches are primarily trained on real-world driving data…
LaST-VLA: Thinking in Latent Spatio-Temporal Space for Vision-Language-Action in Autonomous Driving
Yuechen Luo, Fang Li, Shaoqing Xu +10
While Vision-Language-Action (VLA) models have revolutionized autonomous driving by unifying perception and planning, their reliance on explicit textual Chain-of-Thought (CoT) lead…
UFO: Unifying Feed-Forward and Optimization-based Methods for Large Driving Scene Modeling
Kaiyuan Tan, Yingying Shen, Mingfei Tu +5
Dynamic driving scene reconstruction is critical for autonomous driving simulation and closed-loop learning. While recent feed-forward methods have shown promise for 3D reconstruct…
From Pairs to Sequences: Track-Aware Policy Gradients for Keypoint Detection
Yepeng Liu, Hao Li, Liwen Yang +8
Keypoint-based matching is a fundamental component of modern 3D vision systems, such as Structure-from-Motion (SfM) and SLAM. Most existing learning-based methods are trained on im…
Pixel-Perfect Visual Geometry Estimation
Gangwei Xu, Haotong Lin, Hongcheng Luo +6
Recovering clean and accurate geometry from images is essential for robotics and augmented reality. However, existing geometry foundation models still suffer severely from flying p…
ParkGaussian: Surround-view 3D Gaussian Splatting for Autonomous Parking
Xiaobao Wei, Zhangjie Ye, Yuxiang Gu +10
Parking is a critical task for autonomous driving systems (ADS), with unique challenges in crowded parking slots and GPS-denied environments. However, existing works focus on 2D pa…
Mirage: One-Step Video Diffusion for Photorealistic and Coherent Asset Editing in Driving Scenes
Shuyun Wang, Haiyang Sun, Bing Wang +2
Vision-centric autonomous driving systems rely on diverse and scalable training data to achieve robust performance. While video object editing offers a promising path for data augm…
DGGT: Feedforward 4D Reconstruction of Dynamic Driving Scenes using Unposed Images
Xiaoxue Chen, Ziyi Xiong, Yuantao Chen +11
Autonomous driving needs fast, scalable 4D reconstruction and re-simulation for training and evaluation, yet most methods for dynamic driving scenes still rely on per-scene optimiz…
DriveLaW:Unifying Planning and Video Generation in a Latent Driving World
Tianze Xia, Yongkang Li, Lijun Zhou +9
World models have become crucial for autonomous driving, as they learn how scenarios evolve over time to address the long-tail challenges of the real world. However, current approa…
Pixel-Perfect Depth with Semantics-Prompted Diffusion Transformers
Gangwei Xu, Haotong Lin, Hongcheng Luo +11
This paper presents Pixel-Perfect Depth, a monocular depth estimation model based on pixel-space diffusion generation that produces high-quality, flying-pixel-free point clouds fro…
ViSE: A Systematic Approach to Vision-Only Street-View Extrapolation
Kaiyuan Tan, Yingying Shen, Haiyang Sun +3
Realistic view extrapolation is critical for closed-loop simulation in autonomous driving, yet it remains a significant challenge for current Novel View Synthesis (NVS) methods, wh…
Rethinking Driving World Model as Synthetic Data Generator for Perception Tasks
Kai Zeng, Zhanqian Wu, Kaixin Xiong +12
Recent advancements in driving world models enable controllable generation of high-quality RGB videos or multimodal videos. Existing methods primarily focus on metrics related to g…
ExtraGS: Geometric-Aware Trajectory Extrapolation with Uncertainty-Guided Generative Priors
Kaiyuan Tan, Yingying Shen, Haohui Zhu +8
Synthesizing extrapolated views from recorded driving logs is critical for simulating driving scenes for autonomous driving vehicles, yet it remains a challenging task. Recent meth…
DriveMRP: Enhancing Vision-Language Models with Synthetic Motion Data for Motion Risk Prediction
Zhiyi Hou, Enhui Ma, Fang Li +11
Autonomous driving has seen significant progress, driven by extensive real-world data. However, in long-tail scenarios, accurately predicting the safety of the ego vehicle's future…
Genesis: Multimodal Driving Scene Generation with Spatio-Temporal and Cross-Modal Consistency
Xiangyu Guo, Zhanqian Wu, Kaixin Xiong +10
We present Genesis, a unified framework for joint generation of multi-view driving videos and LiDAR sequences with spatio-temporal and cross-modal consistency. Genesis employs a tw…
ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
Yongkang Li, Kaixin Xiong, Xiangyu Guo +12
Recent studies have explored leveraging the world knowledge and cognitive capabilities of Vision-Language Models (VLMs) to address the long-tail problem in end-to-end autonomous dr…
CoGen: 3D Consistent Video Generation via Adaptive Conditioning for Autonomous Driving
Yishen Ji, Ziyue Zhu, Zhenxin Zhu +7
Recent progress in driving video generation has shown significant potential for enhancing self-driving systems by providing scalable and controllable training data. Although pretra…
Uni-Gaussians: Unifying Camera and Lidar Simulation with Gaussians for Dynamic Driving Scenarios
Zikang Yuan, Yuechuan Pu, Hongcheng Luo +7
Ensuring the safety of autonomous vehicles necessitates comprehensive simulation of multi-sensor data, encompassing inputs from both cameras and LiDAR sensors, across various dynam…