In this paper, we propose a deep learning architecture that produces accurate
dense depth for the outdoor scene from a single color image and a sparse depth.
Inspired by the indoor depth completion, our network estimates surface normals
as the intermediate representation to produce dense depth, and can be trained
end-to-end. With a modified encoder-decoder structure, our network effectively
fuses the dense color image and the sparse LiDAR depth. To address outdoor
specific challenges, our network predicts a confidence mask to handle mixed
LiDAR signals near foreground boundaries due to occlusion, and combines
estimates from the color image and surface normals with learned attention maps
to improve the depth accuracy especially for distant areas. Extensive
experiments demonstrate that our model improves upon the state-of-the-art
performance on KITTI depth completion benchmark. Ablation study shows the
positive impact of each model components to the final performance, and
comprehensive analysis shows that our model generalizes well to the input with
higher sparsity or from indoor scenes.

本文提出了一个深度神经网络架构，通过从单个彩色图像和稀疏深度产生准确的密集深度，实现了室外场景的景深感知。该网络以表面法向量为中间表示来产生密集深度，并可进行端到端的训练。利用修改后的编解码器结构，该网络有效地融合了密集的彩色图像和稀疏的 LiDAR 深度。

DeepLiDAR：利用深度表面法向量指导的深度预测方法，基于稀疏 LiDAR 数据和单张彩色图像的室外场景

DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene  from Sparse LiDAR Data and Single Color Image

Image relighting is to change the illumination of an image to a target
illumination effect without known the original scene geometry, material
information and illumination condition. We propose a novel outdoor scene
relighting method, which needs only a single reference image and is based on
material constrained layer decomposition. Firstly, the material map is
extracted from the input image. Then, the reference image is warped to the
input image through patch match based image warping. Lastly, the input image is
relit using material constrained layer decomposition. The experimental results
reveal that our method can produce similar illumination effect as that of the
reference image on the input image using only a single reference image.

本文提出一种基于材质约束层分解的新型室外场景重新光照方法，该方法只需要一张参考图片即可在输入图片上生成类似参考图片的光照效果。