GS2POSE：将 Gaussian Splatting 与 6D 物体位姿估计相结合
GS2POSE: Marry Gaussian Splatting to 6D Object Pose Estimation
AI总结：
提出 GS2POSE，将 3DGS、李代数与 BA 思想结合为位姿可微渲染框架，通过图像对比迭代优化 6D 位姿并更新颜色参数，在三个基准上提升准确率。
摘要：
对三维物体进行准确的 6D 位姿估计是计算机视觉中的一项基础任务，当前研究通常通过在二维图像特征与三维模型特征之间建立对应关系来预测 6D 位姿。然而，这些方法在处理无纹理物体以及光照条件变化时往往面临困难。为克服这些局限，我们提出 GS2POSE，这是一种用于 6D 物体位姿估计的新颖方法。GS2POSE 构建了一种受 Bundle Adjustment（BA，光束法平差）原理启发的位姿回归算法。通过利用 Lie algebra（李代数），我们扩展了 3DGS 的能力，以开发一个位姿可微的渲染流水线，该流水线通过比较输入图像与渲染图像来迭代优化位姿。此外，GS2POSE 会更新 3DGS 模型内的颜色参数，从而增强其对光照变化的适应能力。与以往模型相比，GS2POSE 在 T-LESS、LineMod-Occlusion 和 LineMod 数据集上分别实现了 1.4%、2.8% 和 2.5% 的准确率提升。
英文摘要：
Accurate 6D pose estimation of 3D objects is a fundamental task in computer vision, and current research typically predicts the 6D pose by establishing correspondences between 2D image features and 3D model features. However, these methods often face difficulties with textureless objects and varying illumination conditions. To overcome these limitations, we propose GS2POSE, a novel approach for 6D object pose estimation. GS2POSE formulates a pose regression algorithm inspired by the principles of Bundle Adjustment (BA). By leveraging Lie algebra, we extend the capabilities of 3DGS to develop a pose-differentiable rendering pipeline, which iteratively optimizes the pose by comparing the input image to the rendered image. Additionally, GS2POSE updates color parameters within the 3DGS model, enhancing its adaptability to changes in illumination. Compared to previous models, GS2POSE demonstrates accuracy improvements of 1.4\%, 2.8\% and 2.5\% on the T-LESS, LineMod-Occlusion and LineMod datasets, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
