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          <h1 class="title is-1 publication-title">Diffusion-FS: Multimodal Free-Space Prediction via Diffusion for Autonomous Driving</h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <a href="https://keshav0306.github.io">Keshav Gupta</a><sup>1</sup>,</span>
            <span class="author-block">
              <a href="https://www.linkedin.com/in/tejas-stanley-a6b728156/?originalSubdomain=in">Tejas S. Stanley</a><sup>1</sup>,</span>
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              <a href="https://reachpranjal.github.io/">Pranjal Paul</a><sup>1</sup>,
            </span>
            <span class="author-block">
              <a href="https://scholar.google.co.in/citations?user=0zgDoIEAAAAJ&hl=en">Arun K. Singh</a><sup>2</sup>,
            </span>
            <span class="author-block">
              <a href="https://scholar.google.co.in/citations?user=QDuPGHwAAAAJ&hl=en">K. Madava Krishna</a><sup>1</sup>,
            </span>
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          <div class="is-size-5 publication-authors">
            <span class="author-block">Robotics Research Center, IIIT-Hyderbad<sup>1</sup></span>
            <span class="author-block">The University of Tartu, Estonia<sup>2</sup></span>
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                <div class="is-size-5 publication-authors" style="margin-top: 1rem;">
        <span class="author-block" style="font-weight: bold; color: rgb(246, 24, 105);">Accepted at IROS 2025</span>
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        <span class="author-block" style="font-weight: bold; color: rgb(25, 18, 18);">Hangzhou, China</span>
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      <h2 class="subtitle has-text-centered">
        <span class="dnerf">Diffusion-FS</span> is a self-supervised approach for freespace prediction using monocular camera images. It takes in a dataset of raw driving logs containing image and ego trajectory pairs. Our self-supervised method processes such an
unannotated dataset to generate free-space segments essential for autonomous driving.
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            Drivable Freespace prediction is a fundamental and crucial problem in autonomous driving. Recent works have addressed the problem by representing the entire non-obstacle road regions as the freespace. In contrast our aim is to estimate the driving corridors that are a navigable subset of the entire road region. Unfortunately, existing corridor estimation methods directly assume a BEV centric representation, which is hard to obtain. In contrast, we frame drivable freespace corridor prediction as a pure image perception task, using only monocular camera input. However such a formulation poses several challenges as one doesn’t have the corresponding data for such freespace corridor segments in the image. Consequently, we develop a novel self-supervised approach for freespace sample generation by leveraging future ego trajectories and front-view camera images, making the process of visual corridor estimation dependent on the ego trajectory. We then employ a diffusion process to model the distribution of such segments in the image. However, the existing binary mask based representation for a segment poses many limitations. Therefore, we introduce ContourDiff, a specialized diffusion-based architecture that denoises over contour points rather than relying on binary mask representations, enabling structured and interpretable freespace predictions. We evaluate our approach qualitatively and quantitatively on both <b>nuScenes</b> and <b>CARLA</b>, demonstrating its effectiveness in accurately predicting safe multimodal navigable corridors in the image.
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          <h2 class="title is-3">nuScenes</h2>
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            Comparison of a non generative baseline YOLOv11 and a generative baseline SegDiff for image segmentation through diffusion with our proposed ContourDiff,
            on nuScenes.
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          <h2 class="title is-3">CARLA</h2>
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            Comparison of a non generative baseline YOLOv11 and a generative baseline SegDiff for image segmentation through diffusion with our proposed ContourDiff,
            on CARLA.
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    <h2 class="title">BibTeX</h2>
    <pre><code>@misc{gupta2025diffusionfsmultimodalfreespaceprediction,
      title={Diffusion-FS: Multimodal Free-Space Prediction via Diffusion for Autonomous Driving},
      author={Keshav Gupta and Tejas S. Stanley and Pranjal Paul and Arun K. Singh and K. Madhava Krishna},
      year={2025},
      eprint={2507.18763},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2507.18763},
}
</code></pre>
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