cvpr 深度估计_无监督单目视频深度估计中的uncertainty方法(CVPR'20)
时间: 2023-08-31 13:05:47 浏览: 74
CVPR'20中的论文"Unsupervised Monocular Video Depth Estimation with Enhanced Spatio-Temporal Embeddings"提出了一种新的uncertainty方法,以改善无监督单目视频深度估计。该方法基于spatio-temporal embeddings,将点云和视图嵌入到同一特征空间中,并在这个空间中计算点云和视图之间的距离。通过计算每个点云与其对应视图之间的距离的方差,可以估计点云的深度估计的不确定性。这种uncertainty方法被证明可以有效地检测和减少深度估计中的误差,提高无监督单目视频深度估计的性能。
相关问题
Gatys_Image_Style_Transfer_CVPR_2016_paper代码解读
Gatys et al. (2016) proposed an algorithm for style transfer, which can generate an image that combines the content of one image and the style of another image. The algorithm is based on the neural style transfer technique, which uses a pre-trained convolutional neural network (CNN) to extract the content and style features from the input images.
In this algorithm, the content and style features are extracted from the content and style images respectively using the VGG-19 network. The content features are extracted from the output of one of the convolutional layers in the network, while the style features are extracted from the correlations between the feature maps of different layers. The Gram matrix is used to measure these correlations.
The optimization process involves minimizing a loss function that consists of three components: the content loss, the style loss, and the total variation loss. The content loss measures the difference between the content features of the generated image and the content image. The style loss measures the difference between the style features of the generated image and the style image. The total variation loss is used to smooth the image and reduce noise.
The optimization is performed using gradient descent, where the gradient of the loss function with respect to the generated image is computed and used to update the image. The process is repeated until the loss function converges.
The code for this algorithm is available online, and it is implemented using the TensorFlow library. It involves loading the pre-trained VGG-19 network, extracting the content and style features, computing the loss function, and optimizing the generated image using gradient descent. The code also includes various parameters that can be adjusted, such as the weight of the content and style loss, the number of iterations, and the learning rate.
2022cvpr姿态估计
2022年计算机视觉和模式识别国际会议(CVPR)是计算机视觉领域的顶级会议之一,姿态估计是其中一个热门的研究方向。姿态估计是指从图像或视频中推断出物体或人体的姿态信息,包括位置、方向、角度等。
在2022年的CVPR中,姿态估计方面的研究主要集中在以下几个方向:
1. 基于深度学习的姿态估计方法,如使用卷积神经网络(CNN)或循环神经网络(RNN)等模型进行姿态估计;
2. 基于多传感器融合的姿态估计方法,如使用RGB-D相机、惯性测量单元(IMU)等多种传感器进行数据融合;
3. 基于先验知识的姿态估计方法,如使用人体解剖学知识、运动学约束等先验知识进行姿态估计。