Evaluating Robustness of 3D Gaussian Splatting–Based 6D Camera Pose Refinement Under Degraded Conditions for Lightly Textured Industrial Synthetic Objects

In this paper, 6D camera pose refinement is explored using 3D Gaussian Splatting (3DGS) on lightly textured industrial object datasets. The study employs datasets generated with Unity 3D rendering software, featuring objects such as a bicycle, MiR robot, Tiago robot, and UR robotic arm, each captured with ground-truth intrinsic and extrinsic camera parameters. A 3DGS model is trained to represent each scene, and 6D pose refinement is evaluated using a recent pose optimization approach, iComMa (inverting 3DGS via Comparing and Matching), which aligns rendered and query images. Experiments utilize 20% of the images for testing and 80% for training the 3DGS models. Camera poses are initialized with varying degrees of perturbation (δ) in both rotation and translation to assess the refinement capabilities. Robustness is further evaluated under degraded conditions by applying various types of noise to the query images, including Gaussian noise, salt-and-pepper noise, dilation, and erosion. Results demonstrate reliable pose refinement under photometric noise; however, with structural noise, the method maintains good rotation accuracy but struggles with translation due to changes in geometric features. This approach shows promise for industrial applications, where 3DGS models trained on synthetic datasets can refine camera poses in real-world industrial environments with common noise characteristics.