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PhysPose: Refining 6D Object Poses with Physical Constraints

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arxiv 2503.23587 v1 pith:6U22OATN submitted 2025-03-30 cs.CV cs.RO

Martin Malenický , Martin Cífka , Médéric Fourmy , Louis Montaut , Justin Carpentier , Josef Sivic , Vladimir Petrik This is my paper
classification cs.CVcs.RO
keywords posephysicalestimationphysposesceneapplicationsapproachconstraints
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Accurate 6D object pose estimation from images is a key problem in object-centric scene understanding, enabling applications in robotics, augmented reality, and scene reconstruction. Despite recent advances, existing methods often produce physically inconsistent pose estimates, hindering their deployment in real-world scenarios. We introduce PhysPose, a novel approach that integrates physical reasoning into pose estimation through a postprocessing optimization enforcing non-penetration and gravitational constraints. By leveraging scene geometry, PhysPose refines pose estimates to ensure physical plausibility. Our approach achieves state-of-the-art accuracy on the YCB-Video dataset from the BOP benchmark and improves over the state-of-the-art pose estimation methods on the HOPE-Video dataset. Furthermore, we demonstrate its impact in robotics by significantly improving success rates in a challenging pick-and-place task, highlighting the importance of physical consistency in real-world applications.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Simulation-Ready Cluttered Scene Estimation via Physics-aware Joint Shape and Pose Optimization

    cs.RO 2026-02 unverdicted novelty 7.0 of 10

    SPARCS uses a differentiable contact model and sparse Hessian solver to jointly optimize shapes and poses of up to five interacting objects, producing physically valid simulation-ready reconstructions.

  2. Picasso: Holistic Scene Reconstruction with Physics-Constrained Sampling

    cs.CV 2026-02 unverdicted novelty 6.0 of 10

    Picasso produces multi-object scene reconstructions that are both geometrically accurate and physically plausible by using physics-constrained rejection sampling over an inferred contact graph, outperforming prior met...

  3. Picasso: Holistic Scene Reconstruction with Physics-Constrained Sampling

    cs.CV 2026-02 conditional novelty 6.0 of 10

    Picasso is an inference-time pose corrector that uses physics-constrained rejection sampling and a contact scene graph to make multi-object scene reconstructions physically plausible and often more accurate.

  4. Real-to-Sim for Highly Cluttered Environments via Physics-Consistent Inter-Object Reasoning

    cs.RO 2026-02 unverdicted novelty 5.0 of 10

    A differentiable optimization pipeline uses a contact graph and rigid-body simulation to jointly refine object poses and physical properties, producing physically valid 3D scene reconstructions from single-view RGB-D ...

  5. Can Single-View Mesh Reconstruction Generalize to Robot Camera Rotation?

    cs.CV 2026-06 unverdicted novelty 4.0 of 10

    Single-view mesh reconstruction generalizes poorly to robot camera rotations, inducing MDE distortion and layout drift, while a gravity-aware refinement cuts one-stage layout-orientation error by 47.1%.

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