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Related papers: Map2World: Segment Map Conditioned Text to 3D Worl…

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Semantic scene understanding is crucial for robotics and computer vision applications. In autonomous driving, 3D semantic segmentation plays an important role for enabling safe navigation. Despite significant advances in the field, the…

Computer Vision and Pattern Recognition · Computer Science 2026-03-02 Lucas Nunes , Rodrigo Marcuzzi , Jens Behley , Cyrill Stachniss

3D-consistent image generation from a single 2D semantic label is an important and challenging research topic in computer graphics and computer vision. Although some related works have made great progress in this field, most of the existing…

Computer Vision and Pattern Recognition · Computer Science 2024-03-12 Bo Li , Yi-ke Li , Zhi-fen He , Bin Liu , Yun-Kun Lai

High-definition (HD) maps are essential for autonomous driving systems. Traditionally, an expensive and labor-intensive pipeline is implemented to construct HD maps, which is limited in scalability. In recent years, crowdsourcing and online…

Computer Vision and Pattern Recognition · Computer Science 2024-09-18 Anqi Shi , Yuze Cai , Xiangyu Chen , Jian Pu , Zeyu Fu , Hong Lu

We present a framework for dynamic trajectory generation for autonomous navigation, which does not rely on HD maps as the underlying representation. High Definition (HD) maps have become a key component in most autonomous driving…

Robotics · Computer Science 2022-03-29 David Paz , Hao Xiang , Andrew Liang , Henrik I. Christensen

Pixel2Mesh (P2M) is a classical approach for reconstructing 3D shapes from a single color image through coarse-to-fine mesh deformation. Although P2M is capable of generating plausible global shapes, its Graph Convolution Network (GCN)…

Computer Vision and Pattern Recognition · Computer Science 2024-03-21 Shijie Zhang , Boyan Jiang , Keke He , Junwei Zhu , Ying Tai , Chengjie Wang , Yinda Zhang , Yanwei Fu

The increasing demand for virtual reality applications has highlighted the significance of crafting immersive 3D assets. We present a text-to-3D 360$^{\circ}$ scene generation pipeline that facilitates the creation of comprehensive…

Computer Vision and Pattern Recognition · Computer Science 2024-07-26 Shijie Zhou , Zhiwen Fan , Dejia Xu , Haoran Chang , Pradyumna Chari , Tejas Bharadwaj , Suya You , Zhangyang Wang , Achuta Kadambi

While recent generative models for 2D images achieve impressive visual results, they clearly lack the ability to perform 3D reasoning. This heavily restricts the degree of control over generated objects as well as the possible applications…

Computer Vision and Pattern Recognition · Computer Science 2020-10-26 Dario Pavllo , Graham Spinks , Thomas Hofmann , Marie-Francine Moens , Aurelien Lucchi

End-to-end autonomous driving with vision-only is not only more cost-effective compared to LiDAR-vision fusion but also more reliable than traditional methods. To achieve a economical and robust purely visual autonomous driving system, we…

Computer Vision and Pattern Recognition · Computer Science 2025-02-14 Ziyang Yan , Wenzhen Dong , Yihua Shao , Yuhang Lu , Liu Haiyang , Jingwen Liu , Haozhe Wang , Zhe Wang , Yan Wang , Fabio Remondino , Yuexin Ma

3D human generation is increasingly significant in various applications. However, the direct use of 2D generative methods in 3D generation often results in losing local details, while methods that reconstruct geometry from generated images…

Computer Vision and Pattern Recognition · Computer Science 2025-12-12 Muxin Zhang , Qiao Feng , Zhuo Su , Chao Wen , Zhou Xue , Kun Li

We present LidarDM, a novel LiDAR generative model capable of producing realistic, layout-aware, physically plausible, and temporally coherent LiDAR videos. LidarDM stands out with two unprecedented capabilities in LiDAR generative…

Computer Vision and Pattern Recognition · Computer Science 2025-12-30 Vlas Zyrianov , Henry Che , Zhijian Liu , Shenlong Wang

Building an efficient and physically consistent world model from limited observations is a long standing challenge in vision and robotics. Many existing world modeling pipelines are based on implicit generative models, which are hard to…

Computer Vision and Pattern Recognition · Computer Science 2025-06-06 Wenhao Hu , Xuexiang Wen , Xi Li , Gaoang Wang

3D scene generation seeks to synthesize spatially structured, semantically meaningful, and photorealistic environments for applications such as immersive media, robotics, autonomous driving, and embodied AI. Early methods based on…

Computer Vision and Pattern Recognition · Computer Science 2025-05-09 Beichen Wen , Haozhe Xie , Zhaoxi Chen , Fangzhou Hong , Ziwei Liu

Text-to-3D generation has achieved remarkable success via large-scale text-to-image diffusion models. Nevertheless, there is no paradigm for scaling up the methodology to urban scale. Urban scenes, characterized by numerous elements,…

Computer Vision and Pattern Recognition · Computer Science 2024-04-11 Fan Lu , Kwan-Yee Lin , Yan Xu , Hongsheng Li , Guang Chen , Changjun Jiang

Deep graph generative modeling has proven capable of learning the distribution of complex, multi-scale structures characterizing real-world graphs. However, one of the main limitations of existing methods is their large output space, which…

Machine Learning · Computer Science 2023-06-01 Nathaniel Diamant , Alex M. Tseng , Kangway V. Chuang , Tommaso Biancalani , Gabriele Scalia

We propose a weakly-supervised approach for conditional image generation of complex scenes where a user has fine control over objects appearing in the scene. We exploit sparse semantic maps to control object shapes and classes, as well as…

Computer Vision and Pattern Recognition · Computer Science 2020-11-23 Dario Pavllo , Aurelien Lucchi , Thomas Hofmann

We present Mix3D, a data augmentation technique for segmenting large-scale 3D scenes. Since scene context helps reasoning about object semantics, current works focus on models with large capacity and receptive fields that can fully capture…

Computer Vision and Pattern Recognition · Computer Science 2021-11-30 Alexey Nekrasov , Jonas Schult , Or Litany , Bastian Leibe , Francis Engelmann

Recent advances in 3D scene generation produce visually appealing output, but current representations hinder artists' workflows that require modifiable 3D textured mesh scenes for visual effects and game development. Despite significant…

Computer Vision and Pattern Recognition · Computer Science 2025-12-22 Tobias Sautter , Jan-Niklas Dihlmann , Hendrik P. A. Lensch

Cities, as the essential environment of human life, encompass diverse physical elements such as buildings, roads and vegetation, which continuously interact with dynamic entities like people and vehicles. Crafting realistic, interactive 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-10-23 Yu Shang , Yuming Lin , Yu Zheng , Hangyu Fan , Jingtao Ding , Jie Feng , Jiansheng Chen , Li Tian , Yong Li

Generating coherent and useful image/video scenes from a free-form textual description is technically a very difficult problem to handle. Textual description of the same scene can vary greatly from person to person, or sometimes even for…

Computer Vision and Pattern Recognition · Computer Science 2020-12-01 Faria Huq , Nafees Ahmed , Anindya Iqbal

In this paper, we propose Text2Scene, a model that generates various forms of compositional scene representations from natural language descriptions. Unlike recent works, our method does NOT use Generative Adversarial Networks (GANs).…

Computer Vision and Pattern Recognition · Computer Science 2019-06-11 Fuwen Tan , Song Feng , Vicente Ordonez
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