HGAN: monocular 3D object depth completion method via hierarchical geometric-aware network

Abstract Accurate and dense scene depth perception is critical for applications such as autonomous driving and robotic navigation. However, due to the limited geometric cues provided by inherently sparse depth data acquired from sensors, significant challenges remain in completing depth information by integrating monocular RGB images to reconstruct object depth in a coherent 3D space. Traditional data augmentation strategies lack geometric awareness, often causing depth discontinuities at the foreground-background boundaries, leading to edge blurring and artifacts that distort geometric relationships in mixed regions. Additionally, mainstream depth estimation frameworks focus too much on global features, making it difficult to model the complex spatial relationships between foreground objects and the background, resulting in the loss of foreground details and ambiguity in background depth. To address these challenges, we propose a hierarchical geometric-aware depth completion network (HGAN) that consists of two key modules: the geometric consistency-aware enhancement module (GCAM) and the geometric relationship decomposition modeling module (GRDM). Specifically, the GCAM constructs a geometric consistency map between the foreground and background regions based on depth similarity and employs adaptive weights to guide foreground-background feature fusion. This enhances the boundary modeling capabilities, significantly improving the structural clarity and continuity of the depth map. The GRDM introduces a geometric relationship decomposition mechanism that explicitly separates depth feature mapping into two orthogonal subspaces: Range Space and Null Space. The Range Space models global scene consistency constraints, ensuring the structural coherence of depth estimation, whereas the Null Space focuses on reconstructing local residual details, effectively enhancing the perception of foreground object edges and fine details. The experimental results show that our method outperforms previous approaches in terms of both efficiency and accuracy on the KITTI and NYU-Depth V2 datasets, with HGAN reducing RMSE by 7.5% on KITTI and 2.3% on NYUv2 compared to CompletionFormer.