“Controllable Neural Reconstruction for Autonomous Driving” by Tóth, Kovács, Bendefy, Hortsin and Matuszka – ACM SIGGRAPH HISTORY ARCHIVES

“Controllable Neural Reconstruction for Autonomous Driving” by Tóth, Kovács, Bendefy, Hortsin and Matuszka

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Conference:


Type(s):


Title:

    Controllable Neural Reconstruction for Autonomous Driving

Session/Category Title:   Images, Video & Computer Vision


Presenter(s)/Author(s):



Abstract:


    We introduce an automated pipeline designed for training neural reconstruction models by leveraging sensor streams gathered from a data collection vehicle. Subsequently, our simulator, aiSim, is employed to generate a controllable virtual counterpart of the real-world environment, enabling the replay of scenes in a closed-loop fashion.

References:


    [1]
    Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. 2020. nuScenes: A multimodal dataset for autonomous driving. arxiv:1903.11027 [cs.LG]

    [2]
    Jason Ku, Ali Harakeh, and Steven L Waslander. 2018. In Defense of Classical Image Processing: Fast Depth Completion on the CPU. In 2018 15th Conference on Computer and Robot Vision (CRV). IEEE, 16?22.

    [3]
    William Ljungbergh, Adam Tonderski, Joakim Johnander, Holger Caesar, Kalle ?str?m, Michael Felsberg, and Christoffer Petersson. 2024. NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving. https://doi.org/10.48550/ARXIV.2404.07762

    [4]
    Tam?s Matuszka, Iv?n Barton, ?d?m Butykai, P?ter Hajas, D?vid Kiss, Domonkos Kov?cs, S?ndor Kuns?gi-M?t?, P?ter Lengyel, G?bor N?meth, Levente Pet?, 2023. aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception. In ICLR 2023 Workshop on SR4AD.

    [5]
    Matthew Tancik, Vincent Casser, Xinchen Yan, Sabeek Pradhan, Ben Mildenhall, Pratul Srinivasan, Jonathan T. Barron, and Henrik Kretzschmar. 2022. Block-NeRF: Scalable Large Scene Neural View Synthesis. arXiv (2022).

    [6]
    Matthew Tancik, Ethan Weber, Evonne Ng, Ruilong Li, Brent Yi, Justin Kerr, Terrance Wang, Alexander Kristoffersen, Jake Austin, Kamyar Salahi, Abhik Ahuja, David McAllister, and Angjoo Kanazawa. 2023. Nerfstudio: A Modular Framework for Neural Radiance Field Development. In ACM SIGGRAPH 2023 Conference Proceedings(SIGGRAPH ?23).

    [7]
    Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun. 2018. Open3D: A Modern Library for 3D Data Processing. arXiv:1801.09847 (2018).

    [8]
    Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang, Deqing Sun, and Ming-Hsuan Yang. 2023. DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes. https://doi.org/10.48550/ARXIV.2312.07920


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