Driving with DINO: Vision Foundation Features as a Unified Bridge for Sim-to-Real Generation in Autonomous Driving
Technical University of Munich（慕尼黑技术大学）；Huawei Hilbert Research Center（华为希利伯特研究中心）；Huawei Riemann Lab（华为里曼实验室）；Wuhan University（武汉大学）；University of Science and Technology of China（中国科学技术大学）；Nanjing University（南京大学）
Driven by the emergence of Controllable Video Diffusion, existing Sim2Real methods for autonomous driving video generation typically rely on explicit intermediate representations to bridge the domain gap. However, these modalities face a fundamental Consistency-Realism Dilemma. Low-level signals (e.g., edges, blurred images) ensure precise control but compromise realism by "baking in" synthetic artifacts, whereas high-level priors (e.g., depth, semantics, HDMaps) facilitate photorealism but lack the structural detail required for consistent guidance. In this work, we present Driving with DINO (DwD), a novel framework that leverages Vision Foundation Module (VFM) features as a unified bridge between the simulation and real-world domains. We first identify that these features encode a spectrum of information, from high-level semantics to fine-grained structure. To effectively utilize this, we employ Principal Subspace Projection to discard the high-frequency elements responsible for "texture baking," while concurrently introducing Random Channel Tail Drop to mitigate the structural loss inherent in rigid dimensionality reduction, thereby reconciling realism with control consistency. Furthermore, to fully leverage DINOv3's high-resolution capabilities for enhancing control precision, we introduce a learnable Spatial Alignment Module that adapts these high-resolution features to the diffusion backbone. Finally, we propose a Causal Temporal Aggregator employing causal convolutions to explicitly preserve historical motion context when integrating frame-wise DINO features, which effectively mitigates motion blur and guarantees temporal stability. Project page: https://albertchen98.github.io/DwD-project/
DINO Pre-training for Vision-based End-to-end Autonomous Driving
Vilnius University Institute of Data Science and Digital Technologies（维尔纽斯大学数据科学与数字技术研究所）；Research Institute of Natural and Technological Sciences, Vytautas Magnus University（维陶塔斯马格努斯大学自然与技术科学研究所）
Dino-Diffusion Modular Designs Bridge the Cross-Domain Gap in Autonomous Parking
Bosch Research North America, Bosch Center for AI (BCAI)（博世北美研究部、博世人工智能中心）；Institute for Robotics and Intelligent Machines (IRIM), Georgia Institute of Technology（机器人与智能机器研究所、佐治亚理工学院）；Department of Computer Science, University of Southern California（计算机科学系、南加州大学）
The Hong Kong University of Science and Technology（香港科学与技术大学）；Horizon Robotics（Horizon机器人）；Nanjing University（南京大学）；Hong Kong University（香港大学）；The Chinese University of Hong Kong（香港中文大学）
DriveGen3D: Boosting Feed-Forward Driving Scene Generation with Efficient Video Diffusion
Zhejiang University（浙江大学）；GigaAI（极佳科技）；Tsinghua University（清华大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Peking University（北京大学）；Monash University（墨尔本大学）
S3PT: Scene Semantics and Structure Guided Clustering to Boost Self-Supervised Pre-Training for Autonomous Driving
Qualcomm Technologies International GmbH（高通技术国际有限公司）；Arriver Software AB（Arriver软件公司）；Linköping University（林雪平大学）；Qualcomm SARL（高通法国公司）；Qualcomm Technologies, Inc（高通技术公司）；KTH Royal Institute of Technology（皇家理工学院（瑞典））
DINO-3DRA: Leveraging 2D Foundation Model Semantics for 3D Cerebral Aneurysm Segmentation
University of Manchester（曼彻斯特大学）；Christabel Pankhurst Institute, University of Manchester（曼彻斯特大学克里斯塔贝尔·潘克赫斯特研究所）；NIHR Manchester Biomedical Research Centre（NIHR曼彻斯特生物医学研究中心）；Manchester Academic Health Sciences Centre（曼彻斯特学术健康科学中心）
Generative Planning with 3D-vision Language Pre-training for End-to-End Autonomous Driving
College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；COWAROBOT（酷哇机器人）；School of Electronic Engineering, University of South China（南华大学电子工程学院）
DinoRADE: Full Spectral Radar-Camera Fusion with Vision Foundation Model Features for Multi-class Object Detection in Adverse Weather
Infineon Technologies AG, Austria（英飞凌科技公司，奥地利）；Graz University of Technology, Austria（格拉茨技术大学，奥地利）；Virtual Vehicle Research GmbH, Austria（虚拟车辆研究有限公司，奥地利）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；Xidian University（西安电子科技大学）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Intel Labs China（英特尔中国研究院）；Peking University（北京大学）；Southern University of Science and Technology（南方科技大学）；School of Mechanical Engineering and Automation, Beihang University（北京航空航天大学机械工程及自动化学院）
Targeted Structure Completion for Sparse-View 3D Reconstruction in Autonomous Driving
MoE Key Lab of Artificial Intelligence, Institute of AI, Shanghai Jiao Tong University（教育部人工智能重点实验室，上海交通大学人工智能研究院）；Central Research Institute, Huawei（华为中央研究院）
ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and Comprehension of Corner Cases in Autonomous Driving
Hong Kong University of Science and Technology（香港科学与技术大学）；The Chinese University of Hong Kong（香港中文大学）；Dalian University of Technology（大连理工大学）；TU Delft（代尔夫特理工大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）；Huawei Zurich Research Center（华为苏黎世研究中心）；Huawei IAS BU（华为IAS业务单元）
