Accurate metric depth completion across wide depth ranges is critical for autonomous systems. However, existing methods often struggle to efficiently capture depth features at both close and long ranges, primarily due to the inadequate modeling of fine-grained depth cues specific to different depth ranges. To address these limitations, we propose AirDC, an adaptive iterative depth refinement framework for full-range metric depth completion. The core contributions of our model lie in the design of two key modules. Specifically, we first construct an adaptive fine-grained stereo-LiDAR feature fusion module to fundamentally strengthen the model's capacity to preserve original full-range depth information. Built upon metric-aligned depth volumes (i.e., a 3D representation composed of cubic voxels uniformly partitioned in real-world metric space), this module employs an adaptive sub-voxel depth attention mechanism to enhance sensitivity to subtle depth variations across the full range, thereby both avoiding long-range accuracy degradation introduced by conventional disparity conversion and alleviating the coarse near-range granularity inherent in metric depth representations. Second, we introduce an iterative hypothesis-guided depth refinement module to improve prediction accuracy while maintaining memory efficiency. By integrating multi-scale multi-modal guidance information from depth hypotheses, this module enables explicit and progressive refinement of the initial depth estimation with a small parameter overhead. Experiments on multiple mainstream real-world and synthetic benchmarks demonstrate that AirDC achieves state-of-the-art performance, providing an effective solution for full-range metric depth completion. The code and data are available at https://github.com/yunqidu/AirDC.
Shi et al. (Thu,) studied this question.