TCG-Depth: A Two-Stage Symmetric Confidence-Guided Framework for Transparent Object Depth Completion

While transparent objects present persistent challenges for RGB-D sensing due to complex refraction and reflection effects, these optical phenomena frequently precipitate unreliable depth measurements and compromise depth completion performance. In this paper, we propose TCG-Depth, a two-stage symmetric confidence-guided framework tailored for transparent object depth completion, wherein confidence estimation and depth refinement are synergistically integrated within a structurally consistent architecture. In the first stage, an initial dense depth map is synthesized from RGB images and sparse depth inputs, concomitant with a pixel-wise confidence map that characterizes the spatially-varying reliability of depth predictions via an adaptive thresholding strategy. In the second stage, a confidence-aware depth refinement module is introduced, utilizing the predicted confidence to actively modulate intermediate feature representations. This mechanism enables the selective restoration of low-confidence regions while simultaneously preserving reliable depth information. Experimental results on the benchmark TransCG dataset demonstrate that the proposed framework exhibits superior performance compared to state-of-the-art depth completion methods, particularly in transparent object regions, thereby validating the effectiveness of the symmetric confidence-guided design.