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.