这里是“出门吃三碗饭”本人, 本文章接下来将介绍2020年对Nerf工作的一篇总结论文NEURAL VOLUME RENDERING:NERF AND BEYOND,论文作者是佐治亚理工学院的Frank Dellaert同学和 MIT的Lin Yen-Chen同学,非常感谢两位大佬的总结贡献。 视频解说可以关注B站,即可找到对应视频,另外可以关注《AI知识物语》 公众号获取更多详情信息。
Neural Volumes(NV)[1] 是 Stephen Lombardzai(Facebook) 在 2019 年SIGGRAPH 发表的工作,虽然 NeRF 的影响力是更大的,但是可以确定的是,NV 是 Volume Rendering 在神经渲染方面上的首次应用,并且 NV 的一些思想在之后的许多工作中都有体现,因此本文将会单独对 NV 进行介绍,希望本文能为大家建立一些 Volume Rend...
Besides the COVID-19 pandemic and political upheaval in the US, 2020 was also\nthe year in which neural volume rendering exploded onto the scene, triggered by\nthe impressive NeRF paper by Mildenhall et al. (2020). Both of us have tried to\ncapture this excitement, Frank on a blog ...
简言之,NeRF是3D场景的连续隐式表达,采用了volume rendering采样相机光束上的点合成2D图像。传统的NeRF优化了渲染图与gt的光度误差(photometric loss)。 1. 基于层的颜色校正 不同的相机总是有不同的ISP配置,导致相同区域的图像颜色不一致。使用颜色不一致的图像渲染会导致渲染质量的下降。Urban-NeRF采用了全局线性变...
Beyond NeRF Underwater: Learning Neural Reflectance Fields for True Color Correction of Marine Imagery The proposed neural scene representation is based on a neural reflectance field model, which learns albedos, normals, and volume densities of the under... T Zhang,M Johnson-Roberson - 《IEEE Ro...
The main idea of our algorithm is to make reconstructed scenes compact along individual rays and consistent across rays in the neighborhood. The proposed regularizers can be plugged into most of existing neural volume rendering techniques based on NeRF in a straightforward way. Despite its simplicity...
The key idea of NeRF is to represent the scene as a density field and a radiance field encoded by Multi-layer Perceptron (MLP) networks, and optimize the MLP networks with the differentiable volume rendering technique. Though NeRF is able to achieve phot...
Interactive training and renderingThis codebase comes with an interactive testbed that includes many features beyond our academic publication:Additional training features, such as extrinsics and intrinsics optimization. Marching cubes for NeRF->Mesh and SDF->Mesh conversion. A spline-based camera path ...
2 Rendering Loss Figure 2: Proposed architecture in the single-view case. For a query point x along a target camera ray with view direction d, a corresponding image feature is extracted from the feature volume W via projection and interpolation. This feature is then passed ...
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction. Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, Wenping Wang. NeurIPS 2021. [PDF] [Project] [Github]UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View ...