Estimates the rotation from canonical coords to realworld xyz.翻译

该短语的意思是从规范化坐标系到真实世界的坐标系估计旋转。在3D姿态估计中，PosePrior 算法通常会将关键点的得分图转换成规范化的 3D 坐标，而真实世界中的 3D 坐标与规范化坐标系之间需要进行旋转转换。因此，需要估计从规范化坐标系到真实世界坐标系的旋转矩阵，以便将规范化坐标系中的 3D 姿态转换为真实世界中的 3D 姿态。

相关问题

PosePrior + Viewpoint: Estimates the most likely normalized 3D pose given 2D detections and hand side.翻译

PosePrior + Viewpoint：给定2D检测和手侧面信息，估计最可能的规范化3D姿势。

该短语表示使用 PosePrior 和 Viewpoint 算法来推断手部的规范化 3D 姿势，输入为 2D 检测结果和手的侧面信息，输出为最可能的 3D 姿势。PosePrior 是一个用于推断规范化 3D 坐标的算法，Viewpoint 是一个用于推断手部视角的算法，这两个算法组合起来可以估计手部的规范化 3D 姿势。

from sklearn.linear_model import LogisticRegression

Logistic Regression is a classification algorithm used to predict the probability of a binary outcome (such as yes/no or true/false) based on one or more predictor variables. It is a linear model that uses a logistic function to model the relationship between the predictor variables and the outcome variable.

The logistic function (also called the sigmoid function) is an S-shaped curve that maps any input value to a probability value between 0 and 1. The output of the logistic function can be interpreted as the probability of the binary outcome being true, given the predictor variables.

The logistic regression model estimates the coefficients of the predictor variables to maximize the likelihood of the observed data, using a technique called maximum likelihood estimation. The coefficients can be used to generate predicted probabilities for new observations, and a threshold can be applied to classify the observations into one of the two categories.

Logistic Regression is widely used in various fields, including healthcare, finance, marketing, and social sciences, for predicting outcomes such as disease diagnosis, credit risk, customer churn, and voter preference. It is a simple and interpretable model that can handle both categorical and continuous predictor variables, and can be extended to handle multi-class classification problems.