splitnerf.github.io/index.html at main · zarzarj/splitnerf.github.io · GitHub

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        content="Official project page for SplitNeRF. We achieve geometry, illumination, and material estimation with NeRF via a novel use of the split sum approximation.">
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  <title>SplitNeRF: Split Sum Approximation Neural Field for Joint Geometry, Illumination, and Material Estimation</title>

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          <h1 class="title is-1 publication-title">SplitNeRF: Split Sum Approximation Neural Field for Joint Geometry,
            Illumination, and Material Estimation</h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <a href="https://www.linkedin.com/in/jesus-zarzar-72a893192/">Jesus Zarzar</a>,</span>
            <span class="author-block">
              <a href="https://www.bernardghanem.com">Bernard Ghanem</a></span>
            <span class="author-block">
            <!-- <span class="author-block">
              <a>Jesus Zarzar</a><sup>1</sup>,</span>
            <span class="author-block">
              <a>Bernard Ghanem</a><sup>1</sup>,</span>
            <span class="author-block"> -->
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            <span class="author-block">KAUST</span>
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      <h2 class="subtitle has-text-centered">
        SplitNeRF jointly extracts geometry, illumination, and material
        properties from multi-view images.
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        <h2 class="title is-3">Abstract</h2>
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          <p>
            We present a novel approach for digitizing real-world objects by estimating their geometry, material properties,
            and environmental lighting from a set of posed images with fixed lighting.
          </p>
          <p>
            Our method incorporates into Neural Radiance Field (NeRF) pipelines the split sum approximation
            used with image-basedlighting for real-time physical-based rendering.
            We propose modeling the scene's lighting with a single scene-specific MLP representing
            pre-integrated image-based lighting at arbitrary resolutions.
            We achieve accurate modeling of pre-integrated lighting by exploiting a novel regularizer
            based on efficient Monte Carlo sampling.
            Additionally, we propose a new method of supervising self-occlusion predictions by exploiting
            a similar regularizer based on Monte Carlo sampling.
          </p>
          <p>
            Experimental results demonstrate the efficiency and effectiveness of our approach in
            estimating scene geometry, material properties, and lighting.
            Our method is capable of attaining state-of-the-art relighting quality after
            only ~1 hour of training in a single NVIDIA A100 GPU.
          </p>
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    <h2 class="title">BibTeX</h2>
    <pre><code>@misc{zarzar2023splitnerf,
      title={SplitNeRF: Split Sum Approximation Neural Field for Joint Geometry, Illumination, and Material Estimation},
      author={Jesus Zarzar and Bernard Ghanem},
      year={2023},
      eprint={2311.16671},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}</code></pre>
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