# DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion

arXiv: https://arxiv.org/abs/1902.00761 · HTML: https://mlsanity.com/paper/1902.00761/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2019.

## Built on by (most cited first, 30 of 38)

- [Unsupervised Depth Completion with Calibrated Backprojection Layers](https://mlsanity.com/paper/2108.10531/) (2021)
- [Learning Topology from Synthetic Data for Unsupervised Depth Completion](https://mlsanity.com/paper/2106.02994/) (2021)
- [Deep Depth Completion from Extremely Sparse Data: A Survey](https://mlsanity.com/paper/2205.05335/) (2022)
- [An Adaptive Framework for Learning Unsupervised Depth Completion](https://mlsanity.com/paper/2106.03010/) (2021)
- [DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion](https://mlsanity.com/paper/2211.10994/) (2022)
- [Test-Time Adaptation for Depth Completion](https://mlsanity.com/paper/2402.03312/) (2024)
- [Monitored Distillation for Positive Congruent Depth Completion](https://mlsanity.com/paper/2203.16034/) (2022)
- [AugUndo: Scaling Up Augmentations for Monocular Depth Completion and Estimation](https://mlsanity.com/paper/2310.09739/) (2023)
- [Depth Completion using Geometry-Aware Embedding](https://mlsanity.com/paper/2203.10912/) (2022)
- [Completion as Enhancement: A Degradation-Aware Selective Image Guided Network for Depth Completion](https://mlsanity.com/paper/2412.19225/) (2024)
- [ProtoDepth: Unsupervised Continual Depth Completion with Prototypes](https://mlsanity.com/paper/2503.12745/) (2025)
- [S3: Learnable Sparse Signal Superdensity for Guided Depth Estimation](https://mlsanity.com/paper/2103.02396/) (2021)
- [AGG-Net: Attention Guided Gated-convolutional Network for Depth Image Completion](https://mlsanity.com/paper/2309.01624/) (2023)
- [RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methods](https://mlsanity.com/paper/2405.10357/) (2024)
- [All-day Depth Completion](https://mlsanity.com/paper/2405.17315/) (2024)
- [A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps](https://mlsanity.com/paper/2205.12918/) (2022)
- [PDC: Piecewise Depth Completion utilizing Superpixels](https://mlsanity.com/paper/2107.06711/) (2021)
- [UnCLe: Benchmarking Unsupervised Continual Learning for Depth Completion](https://mlsanity.com/paper/2410.18074/) (2024)
- [Revisiting Deformable Convolution for Depth Completion](https://mlsanity.com/paper/2308.01905/) (2023)
- [Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth Conversion](https://mlsanity.com/paper/2404.07545/) (2024)
- [A Review of 3D Object Detection with Vision-Language Models](https://mlsanity.com/paper/2504.18738/) (2025)
- [ETA: Energy-based Test-time Adaptation for Depth Completion](https://mlsanity.com/paper/2508.05989/) (2025)
- [DVMN: Dense Validity Mask Network for Depth Completion](https://mlsanity.com/paper/2107.06709/) (2021)
- [Adaptive Illumination based Depth Sensing using Deep Superpixel and Soft Sampling Approximation](https://mlsanity.com/paper/2103.12297/) (2021)
- [How to Guide Adaptive Depth Sampling?](https://mlsanity.com/paper/2205.10202/) (2022)
- [Gated Cross-Attention Network for Depth Completion](https://mlsanity.com/paper/2309.16301/) (2023)
- [Self-Guided Instance-Aware Network for Depth Completion and Enhancement](https://mlsanity.com/paper/2105.12186/) (2021)
- [Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching](https://mlsanity.com/paper/2210.01436/) (2022)
- [StarryGazer: Leveraging Monocular Depth Estimation Models for Domain-Agnostic Single Depth Image Completion](https://mlsanity.com/paper/2512.13147/) (2025)
- [MetaComp: Learning to Adapt for Online Depth Completion](https://mlsanity.com/paper/2207.10623/) (2022)

## Abstract (arXiv)

In this paper we propose a convolutional neural network that is designed to upsample a series of sparse range measurements based on the contextual cues gleaned from a high resolution intensity image. Our approach draws inspiration from related work on super-resolution and in-painting. We propose a novel architecture that seeks to pull contextual cues separately from the intensity image and the depth features and then fuse them later in the network. We argue that this approach effectively exploits the relationship between the two modalities and produces accurate results while respecting salient image structures. We present experimental results to demonstrate that our approach is comparable with state of the art methods and generalizes well across multiple datasets.