Zhao Xu

Weijia Zhang, Jindong Han, Zhao Xu et al.

The integration of machine learning techniques has become a cornerstone in the development of intelligent urban services, significantly contributing to the enhancement of urban efficiency, sustainability, and overall livability. Recent advancements in foundational models, such as ChatGPT, have introduced a paradigm shift within the fields of machine learning and artificial intelligence. These models, with their exceptional capacity for contextual comprehension, problem-solving, and task adaptability, present a transformative opportunity to reshape the future of smart cities and drive progress toward Urban General Intelligence (UGI). Despite increasing attention to Urban Foundation Models (UFMs), this rapidly evolving field faces critical challenges, including the lack of clear definitions, systematic reviews, and universalizable solutions. To address these issues, this paper first introduces the definition and concept of UFMs and highlights the distinctive challenges involved in their development. Furthermore, we present a data-centric taxonomy that classifies existing research on UFMs according to the various urban data modalities and types. In addition, we propose a prospective framework designed to facilitate the realization of versatile UFMs, aimed at overcoming the identified challenges and driving further progress in this field. Finally, this paper explores the wide-ranging applications of UFMs within urban contexts, illustrating their potential to significantly impact and transform urban systems. A comprehensive collection of relevant research papers and open-source resources have been collated and are continuously updated at: https://github.com/usail-hkust/Awesome-Urban-Foundation-Models.

Xin He, Haiyun Guo, Kuan Zhu et al.

Lane detection plays an important role in autonomous driving perception systems. As deep learning algorithms gain popularity, monocular lane detection methods based on them have demonstrated superior performance and emerged as a key research direction in autonomous driving perception. The core designs of these algorithmic frameworks can be summarized as follows: (1) Task paradigm, focusing on lane instance-level discrimination; (2) Lane modeling, representing lanes as a set of learnable parameters in the neural network; (3) Global context supplementation, enhancing inference on the obscure lanes; (4) Perspective effect elimination, providing accurate 3D lanes for downstream applications. From these perspectives, this paper presents a comprehensive overview of existing methods, encompassing both the increasingly mature 2D lane detection approaches and the developing 3D lane detection works. Besides, this paper compares the performance of mainstream methods on different benchmarks and investigates their inference speed under a unified setting for fair comparison. Moreover, we present some extended works on lane detection, including multi-task perception, video lane detection, online high-definition map construction, and lane topology reasoning, to offer readers a comprehensive roadmap for the evolution of lane detection. Finally, we point out some potential future research directions in this field. We exhaustively collect the papers and codes of existing works at https://github.com/Core9724/Awesome-Lane-Detection and will keep tracing the research.

Yong Deng, Baoxing Li, Xu Zhao

Reconstructing 3D clothed humans from monocular camera data is highly challenging due to viewpoint limitations and image ambiguity. While implicit function-based approaches, combined with prior knowledge from parametric models, have made significant progress, there are still two notable problems. Firstly, the back details of human models are ambiguous due to viewpoint invisibility. The quality of the back details depends on the back normal map predicted by a convolutional neural network (CNN). However, the CNN lacks global information awareness for comprehending the back texture, resulting in excessively smooth back details. Secondly, a single image suffers from local ambiguity due to lighting conditions and body movement. However, implicit functions are highly sensitive to pixel variations in ambiguous regions. To address these ambiguities, we propose the Spatial-Temporal Transformer (STT) network for 3D clothed human reconstruction. A spatial transformer is employed to extract global information for normal map prediction. The establishment of global correlations facilitates the network in comprehending the holistic texture and shape of the human body. Simultaneously, to compensate for local ambiguity in images, a temporal transformer is utilized to extract temporal features from adjacent frames. The incorporation of temporal features can enhance the accuracy of input features in implicit networks. Furthermore, to obtain more accurate temporal features, joint tokens are employed to establish local correspondences between frames. Experimental results on the Adobe and MonoPerfCap datasets have shown that our method outperforms state-of-the-art methods and maintains robust generalization even under low-light outdoor conditions.