假如说这个正向过程是可导的,则我们可以通过计算这些梯度(Back Propagation),帮助优化场景参数以实现特定目标,比如图像重建、几何反演和材质估计。 (What)像是逆渲染(Inverse Rendering),根据多张图片来推测3D场景的数据(BRDF/Geometry),NeRF[1]和3DGS[2]就属于这一类。 这样的结构也能接入深度学习(Deep Learning),...
如上是Primary visibility的情况,也就是和相机相连的ray对应的渲染效果,类似一面白墙,然后左上部分是绿色的,上图是交界处的一个像素的效果。这里,我们设置一个half-plane函数,作为Heaviside step function (对其求导则是一个Dirac delta function)的参数: 这里,函数α是这个half-plane,α>0是绿色区域,否则为白色区...
如pipeline rendering(实时渲染）和physics-based rendering(离线渲染)，因此其求导过程也差别很多。前者...
Previous differentiable rendering of SDFs did not fully account for visibility gradients and required the use of mask or silhouette supervision, or discretization into a triangle mesh.In this article, we show how to extend the commonly used sphere tracing algorithm so that it additionally outputs a...
It provides a differentiable rendering function and its associated reverse mode differentiation function (a.k.a adjoint function) that provides derivatives of a loss defined on the rendered image with respect to the lightning, the 3D vertices positions and the vertices colors. The core triangle ...
The Chunk function is utilized to divide the u into ua and ue. The ubound is positive and it defines the permissible range for ua and ue. We set ubound to 360°. The azimuth and elevation angles can determine the position of the rendering camera in 3D coordinates. After that, the 3D ...
Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision Michael Niemeyer1,2 Lars Mescheder1,2,3† Michael Oechsle1,2,4 Andreas Geiger1,2 1Max Planck Institute for Intelligent Systems, Tu¨bingen 2University of...
enables higher-order differentiation, which is absent in many other frameworks. The ability to differentiate a function multiple times in any combination of forward and reverse modes significantly eases the implementation of advanced rendering algorithms, such aswarped-area samplingandHessian-Hamiltonian ...
Differentiable Rendering Toolkit. Contribute to facebookresearch/DRTK development by creating an account on GitHub.
tasks of shape-from-silhouette require differentiable rendering operation to construct the loss function for supervision. However, the traditional rendering algorithms (e.g., rasterization and ray tracing [1]) are not differentiable and cannot be directly applied to deep learning framework due to the...