Object Gaussian for Monocular 6D Pose Estimation from Sparse Views
<|reference_start|>Object Gaussian for Monocular 6D Pose Estimation from Sparse Views: Monocular object pose estimation, as a pivotal task in computer vision and robotics, heavily depends on accurate 2D-3D correspondences, which often demand costly CAD models that may not be readily available. Object 3D reconstruction methods offer an alternative, among which recent advancements in 3D Gaussian Splatting (3DGS) afford a compelling potential. Yet its performance still suffers and tends to overfit with fewer input views. Embracing this challenge, we introduce SGPose, a novel framework for sparse view object pose estimation using Gaussian-based methods. Given as few as ten views, SGPose generates a geometric-aware representation by starting with a random cuboid initialization, eschewing reliance on Structure-from-Motion (SfM) pipeline-derived geometry as required by traditional 3DGS methods. SGPose removes the dependence on CAD models by regressing dense 2D-3D correspondences between images and the reconstructed model from sparse input and random initialization, while the geometric-consistent depth supervision and online synthetic view warping are key to the success. Experiments on typical benchmarks, especially on the Occlusion LM-O dataset, demonstrate that SGPose outperforms existing methods even under sparse view constraints, under-scoring its potential in real-world applications.<|reference_end|>
@article{luo2024object,
    title={Object Gaussian for Monocular 6D Pose Estimation from Sparse Views},
    author={Luqing Luo, Shichu Sun, Jiangang Yang, Linfang Zheng, Jinwei Du, Jian
  Liu},
    journal={arXiv preprint arXiv:2409.02581},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02581},
    primaryClass={cs.CV}
}