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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.16579 |
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| _version_ | 1866915457466892288 |
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| author | Du, Yansong Deng, Yutong Zhou, Yuting Jiao, Feiyu Song, Jian Guan, Xun |
| author_facet | Du, Yansong Deng, Yutong Zhou, Yuting Jiao, Feiyu Song, Jian Guan, Xun |
| contents | This paper presents a novel iToF-RGB fusion framework designed to address the inherent limitations of indirect Time-of-Flight (iToF) depth sensing, such as low spatial resolution, limited field-of-view (FoV), and structural distortion in complex scenes. The proposed method first reprojects the narrow-FoV iToF depth map onto the wide-FoV RGB coordinate system through a precise geometric calibration and alignment module, ensuring pixel-level correspondence between modalities. A dual-encoder fusion network is then employed to jointly extract complementary features from the reprojected iToF depth and RGB image, guided by monocular depth priors to recover fine-grained structural details and perform depth super-resolution. By integrating cross-modal structural cues and depth consistency constraints, our approach achieves enhanced depth accuracy, improved edge sharpness, and seamless FoV expansion. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed framework significantly outperforms state-of-the-art methods in terms of accuracy, structural consistency, and visual quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16579 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards High-Precision Depth Sensing via Monocular-Aided iToF and RGB Integration Du, Yansong Deng, Yutong Zhou, Yuting Jiao, Feiyu Song, Jian Guan, Xun Computer Vision and Pattern Recognition This paper presents a novel iToF-RGB fusion framework designed to address the inherent limitations of indirect Time-of-Flight (iToF) depth sensing, such as low spatial resolution, limited field-of-view (FoV), and structural distortion in complex scenes. The proposed method first reprojects the narrow-FoV iToF depth map onto the wide-FoV RGB coordinate system through a precise geometric calibration and alignment module, ensuring pixel-level correspondence between modalities. A dual-encoder fusion network is then employed to jointly extract complementary features from the reprojected iToF depth and RGB image, guided by monocular depth priors to recover fine-grained structural details and perform depth super-resolution. By integrating cross-modal structural cues and depth consistency constraints, our approach achieves enhanced depth accuracy, improved edge sharpness, and seamless FoV expansion. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed framework significantly outperforms state-of-the-art methods in terms of accuracy, structural consistency, and visual quality. |
| title | Towards High-Precision Depth Sensing via Monocular-Aided iToF and RGB Integration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.16579 |