AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training
Invited conference paper presented and published in conference proceedings

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AbstractNeural Radiance Fields (NeRFs) are a powerful representation for modeling a 3D scene as a continuous function. Though NeRF is able to render complex 3D scenes with view-dependent effects, few efforts have been devoted to exploring its limits in a high-resolution setting. Specifically, existing NeRF-based methods face several limitations when reconstructing high-resolution real scenes, including a very large number of parameters, misaligned input data, and overly smooth details. In this work, we conduct the first pilot study on training NeRF with high-resolution data and propose the corresponding solutions: 1) marrying the multilayer perceptron (MLP) with convolutional layers which can encode more neighborhood information while reducing the total number of parameters; 2) a novel training strategy to address misalignment caused by moving objects or small camera calibration errors; and 3) a high-frequency aware loss. Our approach is nearly free without introducing obvious training/testing costs, while experiments on different datasets demonstrate that it can recover more high-frequency details compared with the current state-of-the-art NeRF models.
Acceptance Date19/06/2023
All Author(s) ListYifan Jiang, Peter Hedman, Ben Mildenhall, Dejia Xu, Jonathan T Barron, Zhangyang Wang, Tianfan Xue
Name of ConferenceIEEE/CVF Conference on Computer Vision and Pattern Recognition
Start Date of Conference17/06/2023
End Date of Conference24/06/2023
Place of ConferenceVancouver
Country/Region of ConferenceCanada
Proceedings TitleProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Pages46 - 55
LanguagesEnglish-United States

Last updated on 2024-27-02 at 12:52