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。