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

arXiv: https://arxiv.org/abs/2508.16579 · HTML: https://mlsanity.com/paper/2508.16579/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Monocular Depth Estimation Based On Deep Learning: An Overview](https://mlsanity.com/paper/2003.06620/) (2020)
- [Guided Depth Super-Resolution by Deep Anisotropic Diffusion](https://mlsanity.com/paper/2211.11592/) (2022)
- [Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution](https://mlsanity.com/paper/2006.01469/) (2020)
- [DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image](https://mlsanity.com/paper/2209.13362/) (2022)
- [Deep Attentional Guided Image Filtering](https://mlsanity.com/paper/2112.06401/) (2021)
- [Deep End-to-End Alignment and Refinement for Time-of-Flight RGB-D Module](https://mlsanity.com/paper/1909.07623/) (2019)
- [Learning to Remove Multipath Distortions in Time-of-Flight Range Images for a Robotic Arm Setup](https://mlsanity.com/paper/1601.01750/) (2016)
- [RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methods](https://mlsanity.com/paper/2405.10357/) (2024)
- [Multipath Interference Suppression in Indirect Time-of-Flight Imaging via a Novel Compressed Sensing Framework](https://mlsanity.com/paper/2507.19546/) (2025)

## Built on by (most cited first, 1 of 1)

- [From Cheap to Pro: A Learning-based Adaptive Camera Parameter Network for Professional-Style Imaging](https://mlsanity.com/paper/2510.20550/) (2025)

## Abstract (arXiv)

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.