We introduce differentiable indirection -- a novel learned primitive that
employs differentiable multi-scale lookup tables as an effective substitute for
traditional compute and data operations across the graphics pipeline. We
demonstrate its flexibility on a number of graphics tasks, i.e., geometric and
image representation, texture mapping, shading, and radiance field
representation. In all cases, differentiable indirection seamlessly integrates
into existing architectures, trains rapidly, and yields both versatile and
efficient results.

我们引入了可微间接寻址 —— 一种新颖的学习原语，它使用可微的多尺度查找表作为传统图形流水线中计算和数据操作的有效替代方法。我们展示了它在几个图形任务中的灵活性，即几何和图像表示、纹理映射、着色和辐射场表示。在所有情况下，可微间接寻址可以无缝集成到现有架构中，快速训练，并产生多样化和高效的结果。

具有微分间接性的高效图像表示

Efficient Graphics Representation with Differentiable Indirection

We present a method for generating high-quality watertight manifold meshes
from multi-view input images. Existing volumetric rendering methods are robust
in optimization but tend to generate noisy meshes with poor topology.
Differentiable rasterization-based methods can generate high-quality meshes but
are sensitive to initialization. Our method combines the benefits of both
worlds; we take the geometry initialization obtained from neural volumetric
fields, and further optimize the geometry as well as a compact neural texture
representation with differentiable rasterizers. Through extensive experiments,
we demonstrate that our method can generate accurate mesh reconstructions with
faithful appearance that are comparable to previous volume rendering methods
while being an order of magnitude faster in rendering. We also show that our
generated mesh and neural texture reconstruction is compatible with existing
graphics pipelines and enables downstream 3D applications such as simulation.
Project page: this https URL

本文介绍了一种从多视角输入图像生成高质量的水密流形网格的方法，该方法将神经体分布的几何初始化与可微分光栅化器进一步优化的不同优点结合起来，证明了该方法可以最大程度地准确重建网格并与现有的图形管道兼容，实现了类似模拟的 3D 应用。

NeuManifold：具高效和高品质绘制支持的神经水密流形重建

NeuManifold: Neural Watertight Manifold Reconstruction with Efficient  and High-Quality Rendering Support

We present a modular differentiable renderer design that yields performance
superior to previous methods by leveraging existing, highly optimized hardware
graphics pipelines. Our design supports all crucial operations in a modern
graphics pipeline: rasterizing large numbers of triangles, attribute
interpolation, filtered texture lookups, as well as user-programmable shading
and geometry processing, all in high resolutions. Our modular primitives allow
custom, high-performance graphics pipelines to be built directly within
automatic differentiation frameworks such as PyTorch or TensorFlow. As a
motivating application, we formulate facial performance capture as an inverse
rendering problem and show that it can be solved efficiently using our tools.
Our results indicate that this simple and straightforward approach achieves
excellent geometric correspondence between rendered results and reference
imagery.

通过利用现有的高度优化的硬件图形管道，我们提出了一种模块化可微渲染器设计，其性能优于以前的方法。我们的设计支持现代图形管道中所有关键操作，并在高分辨率下执行用户可编程着色和几何处理。以人脸表演捕捉为例，我们将其定义为一种反渲染问题，并展示了使用我们的工具可以高效地解决这个问题。结果表明，这种简单直接的方法在渲染结果和参考图像之间实现了出色的几何对应关系。