Reliable depth perception is critical for robotic
manipulation, especially for non-Lambertian objects such as
transparent or highly specular surfaces, where raw depth
measurements are often corrupted or missing. These failures
frequently propagate to motion planning, resulting in invalid
grasp poses and execution errors. We propose AISPO, a depth
completion framework that improves depth reliability for ma-
nipulation in challenging sensing conditions. AISPO combines
multi-scale RGB-D feature fusion with an affine-invariant shape
prior to enforce geometric consistency and mitigate catastrophic
depth failures. Unlike methods that focus primarily on average
depth accuracy, our approach emphasizes physical plausibility
and structural integrity of the predicted depth maps. Extensive
benchmark evaluations demonstrate competitive performance
and strong generalization to unseen objects and novel scenes.
Real-world grasping experiments further show that enhanced
depth reliability significantly improves manipulation success
rates, particularly for transparent objects where many existing
methods fail to produce physically usable depth estimates.