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 introduce a neural relighting algorithm for captured indoors scenes, that
allows interactive free-viewpoint navigation. Our method allows illumination to
be changed synthetically, while coherently rendering cast shadows and complex
glossy materials. We start with multiple images of the scene and a 3D mesh
obtained by multi-view stereo (MVS) reconstruction. We assume that lighting is
well-explained as the sum of a view-independent diffuse component and a
view-dependent glossy term concentrated around the mirror reflection direction.
We design a convolutional network around input feature maps that facilitate
learning of an implicit representation of scene materials and illumination,
enabling both relighting and free-viewpoint navigation. We generate these input
maps by exploiting the best elements of both image-based and physically-based
rendering. We sample the input views to estimate diffuse scene irradiance, and
compute the new illumination caused by user-specified light sources using path
tracing. To facilitate the network's understanding of materials and synthesize
plausible glossy reflections, we reproject the views and compute mirror images.
We train the network on a synthetic dataset where each scene is also
reconstructed with MVS. We show results of our algorithm relighting real indoor
scenes and performing free-viewpoint navigation with complex and realistic
glossy reflections, which so far remained out of reach for view-synthesis
techniques.

利用卷积神经网络构建的光照算法，可以实时改变室内场景的光照，并保持了物体的阴影和光泽材质，从而实现自由视点浏览。