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.

本文提出了一种卷积神经网络，它可以基于高分辨率强度图像得出上采样一系列稀疏范围测量的上下文线索，并且在网络中分离、融合两种模态的上下文线索，旨在利用两种模态之间的关系来产生准确的结果，同时尊重显著的图像结构。通过实验结果表明，该方法与当前最先进的方法相媲美，并可在多个数据集上进行有效泛化。

DFuseNet: RGB 和稀疏深度信息的深度融合，用于图像引导的密集深度完成

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

Recently, deep Convolutional Neural Networks (CNN) have demonstrated strong
performance on RGB salient object detection. Although, depth information can
help improve detection results, the exploration of CNNs for RGB-D salient
object detection remains limited. Here we propose a novel deep CNN architecture
for RGB-D salient object detection that exploits high-level, mid-level, and low
level features. Further, we present novel depth features that capture the ideas
of background enclosure and depth contrast that are suitable for a learned
approach. We show improved results compared to state-of-the-art RGB-D salient
object detection methods. We also show that the low-level and mid-level depth
features both contribute to improvements in the results. Especially, F-Score of
our method is 0.848 on RGBD1000 dataset, which is 10.7% better than the second
place.

提出了一种利用高层次、中层次和低层次特征的深度卷积神经网络结构以及适用于学习方法的新颖深度特征，并在 RGB-D 显著目标检测方面表现出比现有方法更好的效果，特别是在 RGBD1000 数据集上，其 F-Score 达到了 0.848，比第二名高出 10.7%。