聊photometric loss之前,我们得大致了解自监督单目深度估计的原理。几乎所有的自监督任务是利用深度预测和相机姿态估计两个模块联合进行的: 联合训练模型 既然是自监督,那么就意味着输入的图像是没有深度标签的,所以我们就无法利用ground truth这个"标准答案”对输出的深度图进行优化。于是,我们利用联合训练的方法,通过输...
,就可以从右图中采样出该点处的颜色信息,理论上采样出来的颜色信息应该与左图中 处的颜色信息相近,基于该约束就可以构造photometric loss来约束左图中深度信息的求解(如何训练网络的)了。 匹配的两张图片,如何匹配呢?只要知道它们之间的位姿变换是不是就可以了,这里是不是又有了其他的问题呢?比如物体在移动的情况?
We then apply a self-supervised photometric loss that relies on the visual consistency between nearby images. We achieve state-of-the-art results on 3D hand-object reconstruction benchmarks and demonstrate that our approach allows us to improve the pose estimation ac...
然而,使用较大的卷积会导致空间域的过平滑,导致分辨率细节的丢失和模糊,这在超分辨率光度立体视觉任务中应该避免。因此,提出了多分支结构,添加1 × 1卷积来保留像素细节。 三种大小的特征分别通过share-weight regressor,输出的分辨率扩大了四倍 loss = 角误差 + 梯度误差 L_{normal} = L_{AE} +\lambda L_{G...
Implementation of ICRA 2019 paper:Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation @inproceedings{shen2019icra, title={Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation}, author={Shen, Tianwei and Luo, Zixin and Zhou, Lei and Deng, Hanyu and Zhang, Runze and Fang...
photometric loss that relies on the visual consistency between nearby images. We achieve state-of-the-art results on 3D hand-object reconstruction benchmarks and demonstrate that our approach allows us to improve the pose estimation accuracy by leveraging in-formation from neighboring frames in low-...
we present a novel approach to learn depth and odometry via unsupervised learning.Our method ameliorates the original photometric loss to enhance the robustness to illumination change in real scenarios.In addition,we propose a new structure of Pose-net and Explainability-net to achieve rotation-...
In the procedure described, a constant low-intensity light, produced by a 50-watt flood lamp, is controlled with a constant-voltage transformer and a rheostat. Light is passed through damaged leaves laid on a specimen stage and is received by a cadmium-sulphide photo-conductive cell. Photocell...
Small differences in r1 and u from those adopted in Table 4 can be associated with model differences, especially our use of the small planet approximation in calculating the light loss (Mandel and Agol 2002); but this does not seriously affect the correlations or general inferences from the ...
(2) deviation from idealized (simple Poisson) pulse-height distributions is primarily due to secondary electron loss, particularly in venetian blind multipliers; (3) pulse-counting provides better signal-to-noise ratio than any other simple detection scheme, and is not far from optimum detection in...