RefDC: depth completion method focused on long-range depth feature refinement

doi:10.13474/j.cnki.11-2246.2026.0611

Abstract

Abstract: [Purposes]In response to the limited depth information of distant objects in stereo-LiDAR fusion features and the issue of error accumulation and amplification in the disparity-to-depth conversion process of existing depth completion models,this paper proposes a depth completion method—RefDC,focused on long-range depth feature refinement.[Methods]Firstly,this paper optimizes the depth voxel representation by constructing a volumetric representation using true metric depth as the voxel dimension,and enhance depth precision through a soft encoding strategy,improving depth detail features.Then,this paper introduces an iterative hypothesis-guided depth refinement module to extract global and local semantic and geometric features.Using a lightweight ConvGRU,the module iteratively refines the depth results and gradually corrects accumulated errors.[Findings]On both real and synthetic datasets,RefDC achieves state-of-the-art accuracy,with overall performance improvements of 4.2%and 11.9%,and a 6.0%enhancement in long-range depth completion.[Conclusions]Compared to traditional methods,RefDC can capture long-range depth features more accurately while significantly reducing computational burden.

Key words: deep learning, depth completion, long-range depth refinement, stereo-LiDAR fusion, multi-modal fusion

CLC Number:

P237

DU Yunqi, SHI Hongyu, ZHANG Hongjuan, LI Bijun. RefDC: depth completion method focused on long-range depth feature refinement[J]. Bulletin of Surveying and Mapping, 2026, 0(6): 67-73.

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http://tb.chinasmp.com/EN/Y2026/V0/I6/67

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