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          <h1 class="title is-size-2 publication-title"><img src="static/images/logo.gif" width="50%"/><br/>Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration</h1>
          <h2 class="title is-size-5 publication-title">IROS 2025</h5>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <a href="https://oasisartisan.github.io/">Omar Alama</a>,</span>
            <span class="author-block">
              <a href="https://avigyanbh.github.io/">Avigyan Bhattacharya</a>,</span>
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              <a href="https://purenothingness24.github.io/">Haoyang He</a>,
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              <a href="https://seungchan-kim.github.io/">Seungchan Kim</a>,
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              <a href="https://haleqiu.github.io/">Yuheng Qiu</a>,
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            <span class="author-block">
              <a href="https://theairlab.org/team/wenshan/">Wenshan Wang</a>,
            </span>
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              <a href="https://cherieho.com/">Cherie Ho</a>,
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              <a href="https://nik-v9.github.io/">Nikhil Keetha</a>,
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              <a href="https://theairlab.org/team/sebastian/">Sebastian Scherer</a>
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            <span class="author-block university-name">Carnegie Mellon University</span>
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      <h2 class="subtitle has-text-centered">
        <span class="dnerf">RayFronts</span> is a real-time semantic mapping system that enables fine-grained scene understanding both within and beyond
        the depth perception range allowing robots to localize and effectively limit search volumes.
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        <span class="dnerf">RayFronts</span> can be queried with open-set images and text within the map and beyond it.
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        <span class="dnerf">RayFronts</span> can aggregate semantic+geometric evidence efficiently at any depth sensing range </br> even with no depth !
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            Open-set semantic mapping is crucial for open-world robots. Current mapping approaches either are limited by the depth range or only map beyond-range entities in constrained settings, where overall they fail to combine within-range and beyond-range observations.
            Furthermore, these methods make a trade-off between fine-grained semantics and efficiency.
            We introduce <b><i>RayFronts</i></b>, a unified representation that enables both dense and beyond-range efficient semantic mapping.
            <b><i>RayFronts</i></b> encodes task-agnostic open-set semantics to both in-range voxels and beyond-range rays encoded at map boundaries, empowering the robot to reduce search volumes significantly and make informed decisions both within & beyond sensory range, while running at <b>8.84 Hz</b> on an Orin AGX.
            Benchmarking the within-range semantics shows that <b><i>RayFronts</i></b>'s fine-grained image encoding provides <b>1.34x</b> zero-shot 3D semantic segmentation performance while improving throughput by <b>16.5x</b>.
            Traditionally, online mapping performance is entangled with other system components, complicating evaluation.
            We propose a planner-agnostic evaluation framework that captures the utility for online beyond-range search and exploration, and show <b><i>RayFronts</i></b> reduces search volume <b>2.2x</b> more efficiently than the closest online baselines.
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          <p>Overview of our online mapping system, <b><i>RayFronts</i></b> is designed for multi-objective & multi-modal open-set querying of both in-range and beyond-range semantic entities. Given posed RGB-D images, we first extract dense features with our fast language-aligned image encoder. Then, posed depth information and features are used to construct a semantic voxel map for in-range queries. In parallel, <b><i>RayFronts</i></b> maintains a VDB-based occupancy map to generate frontiers, which are further associated with multi-directional semantic rays. The semantic ray fronts enables beyond-range querying of open-set concepts in the unmapped region.</p>
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    <h2 class="title is-3 has-text-centered">Online Semantic Mapping</h2>
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            Our proposed planner-agnostic online-mapping evaluation metric captures how online mapping systems <i>correctly cuts down search volume</i>.
          </p>
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            <b><i>RayFronts</i></b> is superior to all baselines across depth ranges empowering both fine-grained localization and beyond-range guidance.
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          <h2 class="title is-3">Offline 3D Open-Vocabulary Semantic Segmentation</h2>

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          <p>
            <b><i>RayFronts</i></b> consistently outper-forms the baselines in mIoU, and achieves SOTA performance beating the next best baselines by <b>+18.07%</b> and
            <b>+9.63%</b> mIoU on Replica and Scannet, respectively, excluding background. RayFronts is also able to handle background seamlessly with its single-forward pass approach while segment-then-encode approaches fall short.
            For outdoor in-the-wild performance on TartanGround, <b><i>RayFronts</i></b> exceeds the performance of
            the baselines by <b>3.36%</b> mIoU
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          <h2 class="title is-3">Encoder & Mapping Throughput Analysis</h2>
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            <i><b>RayFronts</b> provides state-of-the-art mIoU & <b>17.5 Hz</b> throughput on an AGX Orin.</i> It surpasses Trident with <b>1.34x</b> higher mIoU and a <b>16.5x</b> speedup, while achieving <b>1.81x</b> higher mIoU than NACLIP, which operates at a similar throughput. We also evaluate the full mapping throughput and achieve real time performance at <b>8.84 Hz</b> on the AGX Orin.
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        <h2 class="title is-3">How to use in your own project ?</h2>
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          <ul>
            <li>🤖 <b>Guide your robot with semantics within and beyond. </b> RayFronts can be easily deployed as part of your robotics stack as it supports ROS2 inputs for mapping and querying and has robust visualizations.</li>
            <li>🖼️ <b>Use our SOTA encoder.</b> If you are only interested in our open-vocabulary semantic segmentation encoder, then the RayFronts encoder is also available as a standalone file independent of the rest of the codebase. </li>
            <li>🚀 <b>Bootstrap your semantic mapping project.</b> Utilize the modular and clean RayFronts mapping codebase with its supported datasets to build your project.</li>
            <li>💬 Reach out or raise an issue if you face any problems !</li>
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    <pre><code>@INPROCEEDINGS{alama2025rayfronts,
  author={Alama, Omar and Bhattacharya, Avigyan and He, Haoyang and Kim, Seungchan and Qiu, Yuheng and Wang, Wenshan and Ho, Cherie and Keetha, Nikhil and Scherer, Sebastian},
  booktitle={2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  title={RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration},
  year={2025},
  pages={5930-5937},
  doi={10.1109/IROS60139.2025.11245813}}
</code></pre>
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