Nerf 常见的问题是:如果输入的训练图像不足(nerf往往要100多张各个角度训练图像) 会导致一些错误的 几何错误 潜在的原因是 缺乏对 空的空间/不透明的表面(empty space and opaque surfaces)有约束。 所以作者提出了一个损失, 利用 Depth 来 supervise Nerf。 现有的Nerf pipeline 都会先用SfM来估计 相机参数。其实...
笔者个人认为,引入depth是一种常用的优化方法(如在MVS算法中的depth map),不足以作为一个novel的点,但如果把解决的问题从如何提升NERF的效果如何仅用少量图片就能达到不错的NERF效果,那这个就是另一个novel的设定了,若有兴趣可以阅读本文中related work中提到的NERF from few views的topic。 Method 从上述流程图可...
(SFM). Crucially, SFM also produces sparse 3D points that can be used as "free" depth supervision during training: we add a loss to encourage the distribution of a ray's terminating depth matches a given 3D keypoint, incorporating depth uncertainty. DS-NeRF can render better images given ...
Our work is based on the state-of-the-art framework VolSDF, which models 3D scenes by signed distance functions (SDFs), since this is more applicable for surface reconstruction compared to the standard volumetric representation in vanilla NeRFs. For evaluatio...
Depth-supervised NeRF: Fewer Views and Faster Training for Free CVPR, 2022 Kangle Deng1,Andrew Liu2,Jun-Yan Zhu1,Deva Ramanan1,3, 1CMU,2Google,3Argo AI We propose DS-NeRF (Depth-supervised Neural Radiance Fields), a model for learning neural radiance fields that takes advantage of depth ...
Finally, we fix camera poses and employ a NeRF, however, without a neural network,for dense triangulation and geometric verification. Poses,depth adjustments, and triangulated sparse depths are our outputs. Forthe first time, we show self-supervision within 5 frames already benefits SoTA supervised...
Finally, we fix camera poses and employ a NeRF, however, without a neural network,for dense triangulation and geometric verification. Poses,depth adjustments, and triangulated sparse depths are our outputs. Forthe first time, we show self-supervision within 5 frames already benefits SoTA supervised...
Latent Code指的是NERF-W中用到的预处理方法,将每一张图片都编码成latent code,这使得网络可以补偿视图相关的现象,如光照不一致或镜头阴影,这些现象可能会在新视图中导致严重的伪影,尤其是在输入图像很少的情况下。 Summary 把depth invovle进来NERF的过程是一个简洁直观的idea,这篇文章相比DS-NERF而言,更充分地用...