The loss function for training a nerf model is crucial in determining the accuracy and performance of the model. The loss function is used to measure the difference between the predicted output and the actual ground truth. In the case of nerf, the lossfunction typically involves the comparison ...
使用与粗阶段相同的训练损失,但使用更小的权值作为正则化损失,因为作者发现,根据经验,它会带来略好的质量。 Loss Function Loss Function包含三个部分:来自NeRF的 MSE loss 以及两个从 DVGO 中借鉴的 loss。 \mathcal{L}_{\text {photo }}=\frac{1}{|\mathcal{R}|} \sum_{r \in \mathcal{R}}\|\...
Dynamic Neural Radiance Field 没有用到类似GRAF和GIRAFFE的中间特征输出,所以也没有2D neural rendering的部分。HeadNeRF有用到中间特征,有2D neural rendering HeadNeRF的Loss是基于重建,DyER的监督也是来自3DMM后的重建Appendix B:How to extract mesh from NeRF model ...
在 surface reconstruction 中为了让 implicit function 是一个 signed distance function,我们要约束 implicit function 使得导数处处为 1,常常使用 IGR 中引入的 Eikonal Loss: 但是只约束一阶导数是不够的,因为我们只能在离散的点上加约束,对于这样的锯齿型状 /\/\/\/\ 也是满足条件的,但我们并不希望 implicit ...
3.2 Radiance Field Function 在原文中,光射场函数由NeRF模型表示,NeRF模型是一种典型的多层感知器,以编码的3D点和视角方向作为输入,并返回RGBA值作为输出。虽然本文使用的是神经网络,但这里可以使用任何函数逼近器(function approximator)。例如,Yu等人的后续论文Plenoxels使用球面谐波(spherical harmonics)实现了数量级...
Additionally, a new loss function is introduced to achieve a better image quality under low illumination conditions. The experimental results demonstrate the efficiency of the proposed method in enhancing low-light scene images compared with other state-of-the-art methods, with an overall average of...
./configsshould be a good starting point to understand the most important flags of our code. Important event-related flags includeC_thres(-1 for using the normalized loss function),events(boolean),event_only(boolean) andaccumulate_evs(boolean). The scene is assumed to be in[-bound, bound],...
It focuses on estimating the 6DoF pose of objects in complex scenarios.It designs a "coarse-to-fine" inverse NeRF pose prediction framework.A novel NeRF training strategy that enables high-resolution images full-pixel training.New loss function integrates multi-view geometry to improve pose estimatio...
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 fewer ...
We introduce SimLoss, a loss function aimed at regulating the color field of NeRF based on the quantified distribution differences between ground-truth and rendered similarity maps. Unlike previous NeRF models that used driving datasets, our approach does not require additional input, such as sensor...