This paper aims at recovering the shape of a scene with unknown,
non-Lambertian, and possibly spatially-varying surface materials. When the
shape of the object is highly complex and that shadows cast on the surface, the
task becomes very challenging. To overcome these challenges, we propose a
coordinate-based deep MLP (multilayer perceptron) to parameterize both the
unknown 3D shape and the unknown reflectance at every surface point. This
network is able to leverage the observed photometric variance and shadows on
the surface, and recover both surface shape and general non-Lambertian
reflectance. We explicitly predict cast shadows, mitigating possible artifacts
on these shadowing regions, leading to higher estimation accuracy. Our
framework is entirely self-supervised, in the sense that it requires neither
ground truth shape nor BRDF. Tests on real-world images demonstrate that our
method outperform existing methods by a significant margin. Thanks to the small
size of the MLP-net, our method is an order of magnitude faster than previous
CNN-based methods.

本文提出了一种基于深度多层感知器（MLP）的坐标系参数化方法，通过充分利用观察到的光度变化和表面阴影，恢复了表面形状和非兰伯特表面反射，并显式地预测投射阴影，减轻了这些遮挡区域上可能存在的估计误差。测试结果表明，该方法在真实世界图像中优于现有方法，并且由于 MLP 网络较小，相比以前基于 CNN 的方法，其速度提高了一个数量级。

使用神经反射技术进行形状恢复和阴影处理

Neural Reflectance for Shape Recovery with Shadow Handling

Recently, the proposed deep MLP models have stirred up a lot of interest in
the vision community. Historically, the availability of larger datasets
combined with increased computing capacity leads to paradigm shifts. This
review paper provides detailed discussions on whether MLP can be a new paradigm
for computer vision. We compare the intrinsic connections and differences
between convolution, self-attention mechanism, and Token-mixing MLP in detail.
Advantages and limitations of Token-mixing MLP are provided, followed by
careful analysis of recent MLP-like variants, from module design to network
architecture, and their applications. In the GPU era, the locally and globally
weighted summations are the current mainstreams, represented by the convolution
and self-attention mechanism, as well as MLP. We suggest the further
development of paradigm to be considered alongside the next-generation
computing devices.

本文综述了深度 MLP 模型在计算机视觉领域中的应用，从卷积、自注意力机制以及 Token-mixing MLP 的内在联系和区别进行了详细比较，分析了 MLP 变种的优缺点和应用，并提出了下一代计算设备和方法的发展方向。

我们为新的范式转变做好准备了吗？对视觉 Deep MLP 的调查

Are we ready for a new paradigm shift? A Survey on Visual Deep MLP

NeRF synthesizes novel views of a scene with unprecedented quality by fitting
a neural radiance field to RGB images. However, NeRF requires querying a deep
Multi-Layer Perceptron (MLP) millions of times, leading to slow rendering
times, even on modern GPUs. In this paper, we demonstrate that real-time
rendering is possible by utilizing thousands of tiny MLPs instead of one single
large MLP. In our setting, each individual MLP only needs to represent parts of
the scene, thus smaller and faster-to-evaluate MLPs can be used. By combining
this divide-and-conquer strategy with further optimizations, rendering is
accelerated by three orders of magnitude compared to the original NeRF model
without incurring high storage costs. Further, using teacher-student
distillation for training, we show that this speed-up can be achieved without
sacrificing visual quality.

本文提出了一种使用多个小型多层感知器替代单个大型多层感知器的方法，通过分治策略加速深度神经网络渲染，并且不影响视觉质量。