为了更好地解决逆渲染问题, 同时减少对训练数据的依赖, 可微渲染(differentiable rendering) 的方法被提出。可微渲染是一个可以微分求导的渲染过程, 它的正向是渲染, 逆向是求像素对场景参数的微分。由于传统的渲染不可微, 难以设计基于优化和基于深度学习神经网络的逆渲染方法, 可微渲染技术的提出大大地增加了这些方法...
(Goal)避免直接对边缘进行采样,但仍能有效考虑不连续性问题。 (Previous work)Loubet在2019年的研究Reparameterizing Discontinuous Integrands for Differentiable Rendering提出了一个关键概念:通过让采样点和不连续边缘同时移动来处理不连续性。 (Divergence Theorm)从我们大学Calculus课程中可知,这里将Boundary Integral变成...
这里就不得不提到一个概念,那就是反向渲染(Inverse Rendering),它是从图像(image)到场景(model)的渲染函数,它是从 2D 到 3D 的重要途径。传统的渲染可称作正向渲染(Forward Rendering)。 然而反向渲染需要解决的问题太难了,于是图形学就发展出了可微渲染(Differentiable Rendering)这么个新的领域。可微渲染计算场景数...
所以,尽管一条光路不一定是连续的,但整个积分下则是连续的。The integrand of rendering is discontinuous and not differentiable, but the integral is actually differentiable。这也是可微分渲染的理论基础。 接下来,问题变成了如何对积分求导,如下是对应的数学推导,分为连续区间下和非连续的部分。 下面我们试着在渲...
Path-Space Differentiable Rendering 渲染领域最重要的两篇论文,一篇是‘Rendering Equation’,另一篇则是‘Robust Monte Carlo Methods for Light Transport Simulation’,前者奠定了理论基础,后者则涵盖了主要的光纤传输算法,两篇论文可以说是渲染领域的奠基之作。而在可微分渲染领域,个人感觉‘Differentiable Monte Carlo...
Our method predicts the 3D location and meshes of each object in an image using differentiable rendering and a self-supervised objective derived from a pretrained monocular depth estimation network. We use the KITTI 3D object detection dataset to evaluate the accuracy of the method. Experiments ...
First, we infer hierarchical geometry using two networks, which are optimized via the differentiable renderer. We extract wave components from the sequence of inferred geometry through a network equipped with a differentiable ocean model. Then, ocean dynamics can be evolved using the reconstructed wave...
adjusts the projector input image so that the camera can see the projected output image with a much-reduced distortion. Our key contribution is to model the real projection mapping process with a virtual but controllable light simulation and optimize the projector input using differentiable rendering...
Zero-shot 3D shape understanding Differentiable rendering Text–image fusion Information fusion 1. Introduction Three-dimensional (3D) shape understanding is a critical task in the field of computer vision and pattern recognition. It involves analyzing the geometric structure and spatial relationships of ...
In this paper, we present a differentiable rendering approach that leverages these wavefront sensing data to improve exoplanet detection. Our differentiable renderer models wave-based light propagation through a coronagraphic telescope system, allowing gradient-based optimization to significantly improve star...