用3D高斯溅射优化6自由度物体位姿，提升光照和无纹理场景下的精度。
GS2POSE: Marry Gaussian Splatting to 6D Object Pose Estimation
- 基于束调整思想，构建可微渲染流程迭代优化位姿。
- 在T-LESS、LineMod-Occlusion等数据集上分别提升1.4%~2.8%精度。
- 适合处理无纹理、光照变化大的工业物体位姿估计任务。
准确的6自由度物体位姿估计是计算机视觉中的基础任务。现有方法通常通过建立2D图像特征与3D模型特征之间的对应关系来预测位姿，但在纹理缺失或光照变化条件下表现不佳。为此，我们提出GS2POSE，一种新型6D物体位姿估计方法。该方法受束调整（Bundle Adjustment, BA）原理启发，利用李代数将3D高斯溅射（3DGS）扩展为可微渲染流程，通过比较输入图像与渲染图像，迭代优化位姿。同时，GS2POSE在3DGS模型中更新颜色参数，增强对光照变化的适应能力。相比先前模型，其在T-LESS、LineMod-Occlusion和LineMod数据集上的精度分别提升了1.4%、2.8%和2.5%。
原文摘要 · Abstract (English)
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.
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