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...
Depth computation Depth loss Depth-Guided Sampling Experiment I. Depth Prior II.实验效果及指标 Summary Review 在上一篇介绍引入深度优化NERF的文章(DS-NERF) 中,以colmap的稀疏点云分布(假设为高斯分布)为目标,使ray termination distribution逼近真实的点云分布,学习的过程用color loss 加 KL散度的 depth loss...
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding Radiance Fields without Neural Networks TensoRF: Tensorial Radiance Fields Depth-supervised NeRF: Fewer Views and Faster Training for Free Variable Bitrate Neural Fields(压缩)
但相比基于Depth-Supervised NERF的方法,这篇方法放宽了对depth map准确程度的要求,允许coarse depth map不那么准确,这里提到的coarse depth map来自于depth predict model或者是深度相机的输出。这个方法的目的是让NERF学习到的depth map与coarse depth map在local patch上具备一致性,并且加上了约束,从而保证空间上的连...
(Depth-supervised Neural Radiance Fields), a loss for learning radiance fields that takes advantage of readily-available depth supervision. We leverage the fact that current NeRF pipelines require images with known camera poses that are typically estimated by running structure-from-motion (SFM). ...
在CVPR 2023 上,关于 NeRF 的论文数量暴涨,大多数论文都是工程性质的,这是一篇从理论上对基于 SDF 体渲染过程进行分析和改进。作者先声明了体渲染过程很难完全消除偏差,即 rendered depth 和 intersection point 并不一致,原因在 Geo-NeuS 中也分析过了。
针对训练时间慢的问题,Depth-supervised NeRF4使用SFM的稀疏输出监督NeRF,能够实现更少的视角输入和更快的训练速度。 (二)仅考虑静态场景 NeRF方法只考虑了静态场景,无法拓展到动态场景。这一问题主要和单目视频做结合,从单目视频中学习场景的隐式表示。Neural Scene Flow Fields5将动态场景建模为外观、几何体和三维...
Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. (ECCV 2020)[6]. Clement Godard, Oisin Mac Aodha, Michael Firman, Gabriel Brostow. Digging Into Self-Supervised Monocular Depth Estimation. (ICCV 2019)
On top of them, a NeRF-supervised training procedure is carried out, from which we exploit rendered stereo triplets to compensate for occlusions and depth maps as proxy labels. This results in stereo networks capable of predicting sharp and detailed disparity maps. Experimental results show that ...
Moreover, proxy-supervision in the form of rendered depth by NeRF completes our NeRF-Supervised training regime. With it, we can train deep stereo networks by conducting a low-effort collection campaign, and yet obtain state-of-the- art results without requiring any ground-truth label – or ...