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Text2LiDAR: Text-Guided LiDAR Point Cloud Generation via Equirectangular Transformer

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

The complex traffic environment and various weather conditions make the collection of LiDAR data expensive and challenging. Achieving high-quality and controllable LiDAR data generation is urgently needed, controlling with text is a common practice, but there is little research in this field. To this end, we propose Text2LiDAR, the first efficient, diverse, and text-controllable LiDAR data generation model. Specifically, we design an equirectangular transformer architecture, utilizing the designed equirectangular attention to capture LiDAR features in a manner with data characteristics. Then, we design a control-signal embedding injector to efficiently integrate control signals through the global-to-focused attention mechanism. Additionally, we devise a frequency modulator to assist the model in recovering high-frequency details, ensuring the clarity of the generated point cloud. To foster development in the field and optimize text-controlled generation performance, we construct nuLiDARtext which offers diverse text descriptors for 34,149 LiDAR point clouds from 850 scenes. Experiments on uncontrolled and text-controlled generation in various forms on KITTI-360 and nuScenes datasets demonstrate the superiority of our approach. The project can be found at https://github.com/wuyang98/Text2LiDAR.

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Acknowledgements

We are very grateful for the reviewers’ critical and constructive comments. This work was supported in part by the NSFC under Grant No. 62276141, 62176124, 62276144, 62361166670.

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Authors and Affiliations

  1. PCA Lab, Nanjing University of Science and Technology, Nanjing, China

    Yang Wu, Jianjun Qian & Jian Yang

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Jin Xie

  3. School of Intelligence Science and Technology, Nanjing University, Suzhou, China

    Jin Xie

  4. B-DAT and CICAEET, Nanjing University of Information Science and Technology, Nanjing, China

    Kaihua Zhang

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  1. Yang Wu
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  3. Jianjun Qian
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  4. Jin Xie
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Correspondence to Jin Xie .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Hessen, Germany

    Stefan Roth

  4. Princeton University, Palo Alto, CA, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Wu, Y., Zhang, K., Qian, J., Xie, J., Yang, J. (2025). Text2LiDAR: Text-Guided LiDAR Point Cloud Generation via Equirectangular Transformer. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15114. Springer, Cham. https://doi.org/10.1007/978-3-031-72992-8_17

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