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<!DOCTYPE html>
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            <h1 class="title is-1 publication-title">
  <span class="hippo-gradient">HIPPo</span>: <span class="hippo-gradient">H</span>arnessing <span class="hippo-gradient">I</span>mage-to-3D <span class="hippo-gradient">P</span>riors for Model-free Zero-shot 6D <span class="hippo-gradient">Po</span>se Estimation
</h1>

             <h1 class="is-size-3 publication-title">
              RA-L 2025 & ICRA 2026
            </h1>
            <div class="is-size-5 publication-authors">
              <!-- Paper authors -->
              <span class="author-block">
                 <a href="https://yorklyb.github.io/" target="_blank">Yibo Liu</a><sup>*</sup>,</span>
                <span class="author-block">
                   <a href="https://www.linkedin.com/in/frank-zhaodong-jiang-04558b261/?originalSubdomain=ca" target="_blank">Zhaodong Jiang</a><sup>*</sup>,</span>
                  <span class="author-block">
                    <a href="https://binbin-xu.github.io/" target="_blank">Binbin Xu</a></sup>,</span>
                     <span class="author-block">
                    <a href="https://guilewu.github.io/" target="_blank">Guile Wu</a></sup>,</span>
                        <span class="author-block">
                    <a href="https://www.linkedin.com/in/yuan-ren-ba024620/" target="_blank">Yuan Ren</a></sup>,</span>
               <br>
                           <span class="author-block">
                    <a href="https://www.linkedin.com/in/tongtong-cao-9aa07017a/" target="_blank">Tongtong Cao</a></sup>,</span>

                           <span class="author-block">

                    <a href="https://www.linkedin.com/in/bingbing-liu-9185a412/" target="_blank">Bingbing Liu</a></sup>,</span>
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                    <a href="https://www.linkedin.com/in/rhyang6/?originalSubdomain=ca" target="_blank">Rui Heng Yang</a></sup>,</span>
                                 <span class="author-block">
                    <a href="https://aras62.github.io/" target="_blank">Amir Rasouli</a></sup>,</span>
                                    <span class="author-block">
                    <a href="https://www.yorku.ca/jjshan/PROF_SHAN.html" target="_blank">Jinjun Shan</a>
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                <br>
                *Equal Contribution. Work done during an internship at Huawei Noah Ark's Lab.
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       <h2 class="subtitle" style="text-align: justify;">
        Overview of HIPPo. Given a video consisting of RGB-D frames, Grounding DINO \cite{grounding} is first applied to segment the object based on a prompt. Next, the proposed HIPPo Dreamer, built on a multiview Diffusion Model and a 3D reconstruction foundation model, generates a 3D mesh of the object from the first detected frame in a few seconds. Then, the diffusion prior mesh is provided to the pose estimation network to estimate the 6D pose in real time. Meanwhile, the mesh optimization module monitors viewpoint changes through a predefined viewpoint sphere and triggers mesh optimization when the viewpoint varies dramatically. The module then replaces the diffusion prior with more reliable appearance and geometry from online measurements.
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            This work focuses on model-free zero-shot 6D object pose estimation for robotics applications. While existing methods can estimate the precise 6D pose of objects, they heavily rely on curated CAD models or reference images, the preparation of which is a time-consuming and labor-intensive process. Moreover, in real-world scenarios, 3D models or reference images may not be available in advance and instant robot reaction is desired. In this work, we propose a novel framework named HIPPo, which eliminates the need for curated CAD models and reference images by harnessing image-to-3D priors from Diffusion Models, enabling model-free zero-shot 6D pose estimation. Specifically, we construct HIPPo Dreamer, a rapid image-to-mesh model built on a multiview Diffusion Model and a 3D reconstruction foundation model. Our HIPPo Dreamer can generate a 3D mesh of any unseen objects from a single glance in just a few seconds. Then, as more observations are acquired, we propose to continuously refine the diffusion prior mesh model by joint optimization of object geometry and appearance. This is achieved by a measurement-guided scheme that gradually replaces the plausible diffusion priors with more reliable online observations. Consequently, HIPPo can instantly estimate and track the 6D pose of a novel object and maintain a complete mesh for immediate robotic applications. Thorough experiments on various benchmarks show that HIPPo outperforms state-of-the-art methods in 6D object pose estimation when prior reference images are limited.
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      The task is to grasp a novel object while also obtaining its 3D oriented bounding box for further planning. The frames are captured by a static calibrated RGB-D camera. If we employ FoundationPose for this task, we have to reconstruct the object using BundleSDF first, which could take several minutes. In comparison, by leveraging HIPPo, the robot only needs to wait a few seconds to execute the task.
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        Thanks to the utilization of the image-to-3D prior, HIPPo demonstrates robust zero-shot ability on unseen objects.
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<div style="border: 1px solid #ccc; padding: 1em; border-radius: 8px; max-width: 800px; background-color: #f9f9f9; font-family: sans-serif; margin: 2rem auto; text-align: center;">
  <strong>Citation:</strong>
  <pre id="citation-text" style="white-space: pre-wrap; font-family: monospace; background-color: #fff; padding: 0.5em; border-radius: 4px; border: 1px solid #ddd; text-align: left;">
@ARTICLE{11063421,
  author={Liu, Yibo and Jiang, Zhaodong and Xu, Binbin and Wu, Guile and Ren, Yuan and Cao, Tongtong and Liu, Bingbing and Yang, Rui Heng and Rasouli, Amir and Shan, Jinjun},
  journal={IEEE Robotics and Automation Letters},
  title={HIPPo: Harnessing Image-to-3D Priors for Model-Free Zero-Shot 6D Pose Estimation},
  year={2025},
  volume={10},
  number={8},
  pages={8284-8291},
  keywords={Solid modeling;Three-dimensional displays;Pose estimation;Diffusion models;Robots;Image reconstruction;Optimization;Adaptation models;Reliability;Geometry;Computer vision;diffusion models;generative AI;pose estimation},
  doi={10.1109/LRA.2025.3585384}}
  </pre>
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