Latent Appearance Modeling 还是用到了GLO的方法(generative latent optimization),对每张图生成一个相关的latent code也叫做appearance embedding。 把原始NeRF中的c(t)换成了ci(t),这个意思是说,要强调颜色c对每张图的依赖性。他认为原来的c和图片标号没有关系,但是在这里ci介入了一种像素对应图片的依赖性,因为每...
简介:NeRF系列(2):NeRF in the wild : Neural Radiance Fields for Unconstrained Photo Collections论文解读与公式推导 NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections 论文: https://openaccess.thecvf.com/content/CVPR2021/papers/Martin-Brualla_NeRF_in_the_Wild_Neural_Radiance...
NeRF网络结构上,借鉴了Mip-NeRF;相机位姿的优化借鉴了BARF的方法;另外使用了NeRF in the wild中的外观嵌入。 Block Size and Placement 论文中的方式是,选择在道路交叉口放置每个Block-NeRF,能覆盖临近道路段75%的距离,同时保证相邻Block-NeRF有50%的重合。 个人想法:这个场景完全是网格化的街区,如果街区形状不规则...
Update:NeRF-W(NeRF in the Wild) implementation is added tonerfwbranch! Update: The lastest code (using the latest libraries) will be updated todevbranch. The master branch remains to support the colab files. If you don't use colab, it is recommended to switch to dev branch. Only issues...
The code is largely based on NeRF implementation (see master or dev branch), the main difference is the model structure and the rendering process, which can be found in the two files under models/. 💻 Installation Hardware OS: Ubuntu 18.04 NVIDIA GPU with CUDA>=10.2 (tested with 1 RTX20...
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它基于渲染加速,还做了两个优化工作,分别是empty space skipping以及hybrid volume-surface rendering,这两个措施其实属于常规操作,其中volume-surface rendering在 Neural 3D Reconstruction in the Wild 这篇工作中看到类似的,可以理解为先求光线与物体相交的位置(hit point),再基于hit point向光线方向进行采样,这样...
NeRF-W (“in the wild”), an influential early paper, proposed two ideas: Exclude transient objects by training two scenes: a common static scene (shared between all views) and a transient scene (one per each view). Introduce alighting latent variable, a multidimensional vector to encode the...
We incorporate tech- niques from NeRF in the Wild (NeRF-W) [38], which adds a latent code per training image to handle inconsistent scene appearance when applying NeRF to landmarks from the Photo Tourism dataset. NeRF-W creates a separate NeRF for each landmark from thousan...