GS-Pose: Generalizable Segmentation-based 6D Object Pose Estimation with 3D Gaussian Splatting
<|reference_start|>GS-Pose: Generalizable Segmentation-based 6D Object Pose Estimation with 3D Gaussian Splatting: This paper introduces GS-Pose, a unified framework for localizing and estimating the 6D pose of novel objects. GS-Pose begins with a set of posed RGB images of a previously unseen object and builds three distinct representations stored in a database. At inference, GS-Pose operates sequentially by locating the object in the input image, estimating its initial 6D pose using a retrieval approach, and refining the pose with a render-and-compare method. The key insight is the application of the appropriate object representation at each stage of the process. In particular, for the refinement step, we leverage 3D Gaussian splatting, a novel differentiable rendering technique that offers high rendering speed and relatively low optimization time. Off-the-shelf toolchains and commodity hardware, such as mobile phones, can be used to capture new objects to be added to the database. Extensive evaluations on the LINEMOD and OnePose-LowTexture datasets demonstrate excellent performance, establishing the new state-of-the-art. Project page: https://dingdingcai.github.io/gs-pose.<|reference_end|>
@article{cai2024gs-pose:,
    title={GS-Pose: Generalizable Segmentation-based 6D Object Pose Estimation with
  3D Gaussian Splatting},
    author={Dingding Cai, Janne Heikkil"a, Esa Rahtu},
    journal={arXiv preprint arXiv:2403.10683},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10683},
    primaryClass={cs.CV}
}