publications

2024

1. 

   FocalPose++: Focal Length and Object Pose Estimation via Render and Compare

   Martin Cífka, Georgy Ponimatkin, Yann Labbé, and 4 more authors

   IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

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   We introduce FocalPose++, a neural render-and-compare method for jointly estimating the camera-object 6D pose and camera focal length given a single RGB input image depicting a known object. The contributions of this work are threefold. First, we derive a focal length update rule that extends an existing state-of-the-art render-and-compare 6D pose estimator to address the joint estimation task. Second, we investigate several different loss functions for jointly estimating the object pose and focal length. We find that a combination of direct focal length regression with a reprojection loss disentangling the contribution of translation, rotation, and focal length leads to improved results. Third, we explore the effect of different synthetic training data on the performance of our method. Specifically, we investigate different distributions used for sampling object’s 6D pose and camera’s focal length when rendering the synthetic images, and show that parametric distribution fitted on real training data works the best. We show results on three challenging benchmark datasets that depict known 3D models in uncontrolled settings. We demonstrate that our focal length and 6D pose estimates have lower error than the existing state-of-the-art methods.

   @article{cifka2024focalpose++,
     title = {{F}ocal{P}ose++: {F}ocal {L}ength and {O}bject {P}ose {E}stimation via {R}ender and {C}ompare},
     author = {C{\'i}fka, Martin and Ponimatkin, Georgy and Labb{\'e}, Yann and Russell, Bryan and Aubry, Mathieu and Petrik, Vladimir and Sivic, Josef},
     journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
     year = {2024},
     publisher = {IEEE},
     pages = {1-17},
     doi = {10.1109/TPAMI.2024.3475638},
   }