SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion

[2204.13635] SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2204.13635 (cs) [Submitted on 28 Apr 2022 ( v1 ), last revised 15 Apr 2026 (this version, v2)] Title: SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion Authors: Danish Nazir , Marcus Liwicki , Didier Stricker , Muhammad Zeshan Afzal View a PDF of the paper titled SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion, by Danish Nazir and 3 other authors View PDF HTML (experimental) Abstract: Depth completion involves recovering a dense depth map from a sparse map and an RGB image. Recent approaches focus on utilizing color images as guidance images to recover depth at invalid pixels. However, color images alone are not enough to provide the necessary semantic understanding of the scene. Consequently, the depth completion task suffers from sudden illumination changes in RGB images (e.g., shadows). In this paper, we propose a novel three-branch backbone comprising color-guided, semantic-guided, and depth-guided branches. Specifically, the color-guided branch takes a sparse depth map and RGB image as an input and generates color depth which includes color cues (e.g., object boundaries) of the scene. The predicted dense depth map of color-guided branch along-with semantic image and sparse depth map is passed as input to semantic-guided branch for estimating semantic depth. The depth-guided branch takes sparse, color, and semantic depths to generate the dense depth map. The color depth, semantic depth, and guided depth are adaptively fused to produce the output of our proposed three-branch backbone. In addition, we also propose to apply semantic-aware multi-modal attention-based fusion block (SAMMAFB) to fuse features between all three branches. We further use CSPN++ with Atrous convolutions to refine the dense depth map produced by our three-branch backbone. Extensive experiments show that our model achieves state-of-the-art performance in the KITTI depth completion benchmark at the time of submission. Comments: accepted at IEEE Access Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2204.13635 [cs.CV] (or arXiv:2204.13635v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2204.13635 Focus to learn more arXiv-issued DOI via DataCite Journal reference: IEEE Access, vol. 10, pp. 120781-120791, 2022 Related DOI : https://doi.org/10.1109/ACCESS.2022.3214316 Focus to learn more DOI(s) linking to related resources Submission history From: Danish Nazir [ view email ] [v1] Thu, 28 Apr 2022 16:53:25 UTC (2,278 KB) [v2] Wed, 15 Apr 2026 07:36:14 UTC (16,250 KB) Full-text links: Access Paper: View a PDF of the paper titled SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion, by Danish Nazir and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2022-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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