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

## TL;DR

This paper introduces DFuseNet, a neural network that fuses RGB images and sparse depth data to produce dense depth maps, leveraging separate feature extraction and fusion for improved accuracy.

## Contribution

The novel architecture separately extracts and then fuses contextual cues from intensity images and depth features, enhancing depth completion performance.

## Key findings

- Achieves results comparable to state-of-the-art methods.
- Generalizes well across multiple datasets.

## Abstract

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.