Neural reflectance models are capable of accurately reproducing the
spatially-varying appearance of many real-world materials at different scales.
However, existing methods have difficulties handling highly glossy materials.
To address this problem, we introduce a new neural reflectance model which,
compared with existing methods, better preserves not only specular highlights
but also fine-grained details. To this end, we enhance the neural network
performance by encoding input data to frequency space, inspired by NeRF, to
better preserve the details. Furthermore, we introduce a gradient-based loss
and employ it in multiple stages, adaptive to the progress of the learning
phase. Lastly, we utilize an optional extension to the decoder network using
the Inception module for more accurate yet costly performance. We demonstrate
the effectiveness of our method using a variety of synthetic and real examples.

通过将输入数据编码到频率空间来增强神经网络性能，并引入基于梯度的损失在多个阶段使用，使得这种新型神经反射模型在处理高度光泽材料时能够更好地保留镜面高光并提高细节的保留效果。我们最后还使用 Inception 模块作为解码器网络的可选扩展，以获得更精确但成本更高的性能。我们通过使用各种合成和真实示例来证明我们方法的有效性。

一种准确性更好的改进型 NeuMIP

An Improved NeuMIP with Better Accuracy

We present a method for turning a flash selfie taken with a smartphone into a
photograph as if it was taken in a studio setting with uniform lighting. Our
method uses a convolutional neural network trained on a set of pairs of
photographs acquired in an ad-hoc acquisition campaign. Each pair consists of
one photograph of a subject's face taken with the camera flash enabled and
another one of the same subject in the same pose illuminated using a
photographic studio-lighting setup. We show how our method can amend defects
introduced by a close-up camera flash, such as specular highlights, shadows,
skin shine, and flattened images.

通过卷积神经网络对一组拍摄自拍照以及参考照片进行训练，我们提出了一种将手机自拍照修正为拍摄于摄影棚内的统一照明压缩照片的方法，并成功改善了因自拍照引入的高光、阴影、皮肤过于光滑等缺陷。

DeepFlash: 将自拍照片编辑成专业风格肖像照

DeepFlash: Turning a Flash Selfie into a Studio Portrait

We consider the non-Lambertian object intrinsic problem of recovering diffuse
albedo, shading, and specular highlights from a single image of an object.
We build a large-scale object intrinsics database based on existing 3D models
in the ShapeNet database. Rendered with realistic environment maps, millions of
synthetic images of objects and their corresponding albedo, shading, and
specular ground-truth images are used to train an encoder-decoder CNN. Once
trained, the network can decompose an image into the product of albedo and
shading components, along with an additive specular component.
Our CNN delivers accurate and sharp results in this classical inverse problem
of computer vision, sharp details attributed to skip layer connections at
corresponding resolutions from the encoder to the decoder. Benchmarked on our
ShapeNet and MIT intrinsics datasets, our model consistently outperforms the
state-of-the-art by a large margin.
We train and test our CNN on different object categories. Perhaps surprising
especially from the CNN classification perspective, our intrinsics CNN
generalizes very well across categories. Our analysis shows that feature
learning at the encoder stage is more crucial for developing a universal
representation across categories.
We apply our synthetic data trained model to images and videos downloaded
from the internet, and observe robust and realistic intrinsics results. Quality
non-Lambertian intrinsics could open up many interesting applications such as
image-based albedo and specular editing.

该研究利用大规模的对象固有数据库基于现有的 3D 模型建立，使用以真实环境图为基础的渲染器，训练 CNN，将图像分解为表面反照率，阴影成分以及一种额外的反射成分，结果准确且清晰，可优化计算机视觉中的经典反问题。