Towards High-Precision Depth Sensing via Monocular-Aided iToF and RGB Integration

• URL: http://arxiv.org/abs/2508.16579v1
• Date: Sun, 03 Aug 2025 13:48:00 GMT
• Title: Towards High-Precision Depth Sensing via Monocular-Aided iToF and RGB Integration
• Authors: Yansong Du, Yutong Deng, Yuting Zhou, Feiyu Jiao, Jian Song, Xun Guan,
• Abstract summary: We present a novel iToF-RGB fusion framework designed to address the inherent limitations of indirect Time-of-Flight (iToF) depth sensing.<n>The proposed method first reprojects the narrow-FoV iToF depth map onto the wide-FoV RGB coordinate system.<n>A dual-encoder fusion network is then employed to jointly extract complementary features from the reprojected iToF depth and RGB image.<n>By integrating cross-modal structural cues and depth consistency constraints, our approach achieves enhanced depth accuracy, improved edge sharpness, and seamless FoV expansion.
• Score: 11.077863605272668
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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