To enhance perception performance in complex and extensive scenarios within the realm of autonomous driving, there has been a noteworthy focus on temporal modeling, with a particular emphasis on streaming methods. The prevailing trend in streaming models involves the utilization of stream queries for the propagation of temporal information. Despite the prevalence of this approach, the direct application of the streaming paradigm to the construction of vectorized high-definition maps (HD-maps) fails to fully harness the inherent potential of temporal information. This paper introduces the Stream Query Denoising (SQD) strategy as a novel approach for temporal modeling in high-definition map (HD-map) construction. SQD is designed to facilitate the learning of temporal consistency among map elements within the streaming model. The methodology involves denoising the queries that have been perturbed by the addition of noise to the ground-truth information from the preceding frame. This denoising process aims to reconstruct the ground-truth information for the current frame, thereby simulating the prediction process inherent in stream queries. The SQD strategy can be applied to those streaming methods (e.g., StreamMapNet) to enhance the temporal modeling. The proposed SQD-MapNet is the StreamMapNet equipped with SQD. Extensive experiments on nuScenes and Argoverse2 show that our method is remarkably superior to other existing methods across all settings of close range and long range. The code will be available soon.
DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；National Engineering Research Center of Optical Instrumentation, Zhejiang University（浙江大学光学仪器国家工程研究中心）；College of Computer Science, Zhejiang University（浙江大学计算机学院）；Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院人形机器人与机器人学研究所）；USC Viterbi School of Engineering, the University of Southern California（南加州大学维特比工程学院）
NavMapFusion: Diffusion-based Fusion of Navigation Maps for Online Vectorized HD Map Construction
Mercedes-Benz Research & Development North America, USA（梅赛德斯-奔驰北美研究与开发）；University of Stuttgart, Germany（斯图加特大学）；University of California, San Diego, USA（加州大学圣地亚哥分校）；University of Southampton, United Kingdom（南安普顿大学）
AvatarForcing: One-Step Streaming Talking Avatars via Local-Future Sliding-Window Denoising
Zhejiang University（浙江大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Kling Team, Kuaishou Technology（快手科技 Kling 团队）；Tsinghua University（清华大学）
Hierarchical Denoising For Multi-Step Visual Reasoning
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（北京大学计算机科学学院多媒体信息处理技术国家重点实验室）；The Hong Kong University of Science and Technology（香港科技大学）；Beihang University（北京航空航天大学）；Fuzhou University（福州大学）；Muka Robotics（木卡机器人）
StreamDiffusion: A Pipeline-level Solution for Real-time Interactive Generation
UC Berkeley（伯克利大学）；University of Tsukuba（筑波大学）；International Christian University（国际基督教大学）；Toyo University（东洋大学）；Tokyo Institute of Technology（东京技术大学）；Tohoku University（东北大学）；MIT（麻省理工学院）
ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Streaming Autoregressive Video Generation via Diagonal Distillation
South China University of Technology（南方科技大学）；Westlake University（西湖大学）；Johns Hopkins University（约翰霍普金斯大学）；University of California, Merced（加州大学默塞德分校）；The Chinese University of Hong Kong（香港中文大学）
