6DoF pose 估计evaluation metrics 汇总,持续更新~~
转载请注明作者和出处: http://blog.csdn.net/john_bh/
文章目录
- 2D-projection error
- ADD
- 3.ADD-S
- Visible Surface Discrepancy (VSD)
1. 2D-projection error
2D-projection error measures the average distance between the 2D projections in the image space of the 3D model points, transformed using the ground-truth pose and the predicted pose. The pose estimate is considered to be correct if it is within a selected threshold. 2D-Proj denotes the percentage of correctly estimated poses using a 2D Projection Error threshold set to 5 pixels. For symmetric objects, the 2D projection error is computed against all possible ground truth poses, and the lowest value is used.
2D-projection error 是测量 3D 模型点图像空间中 2D 投影之间的平均距离,使用 ground truth 姿势和预测姿势进行转换。 如果姿态估计在选定的阈值内,则认为姿态估计是正确的。 2D-Proj 表示使用 2D 投影误差阈值设置为 5 像素的正确估计姿势的百分比
。 对于对称对象,2D 投影误差是针对所有可能的 ground truth姿态计算的,并使用最小值。
P r o j . 2 D = 1 m ∑ x ∈ M ∣ ∣ K ( R x + t ) − K ( R ^ x + t ^ ) ∣ ∣ Proj.2D = \frac{1}{m}\sum_{x\in M}||K(Rx+t)-K(\hat Rx+\hat t)|| Proj.2D=m1x∈M∑∣∣K(Rx+t)−K(R^x+t^)∣∣
2. ADD
paper:Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes
ADD measures the average distance between the 3D model points transformed using the ground-truth pose and the predicted pose.
For ADD metric, a pose is regarded as correct if the average distance of model points is less than 10% of the model diameter.
ADD测量使用 ground truth 姿势和预测姿势转换的 3D 模型点之间的平均距离。对于 ADD 度量,如果模型点的平均距离小于模型直径的 10%,则认为姿势正确
。
A D D = 1 m ∑ x ∈ M ∣ ∣ ( R x + t ) − ( R ^ x + t ^ ) ∣ ∣ ADD = \frac{1}{m}\sum_{x\in M}||(Rx+t)-(\hat Rx+\hat t)|| ADD=m1x∈M∑∣∣(Rx+t)−(R^x+t^)∣∣
3.ADD-S
RSS 2018:PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
A D D = 1 m ∑ x 1 ∈ M min x 2 ∈ M ∣ ∣ ( R x 1 + t ) − ( R ^ x 2 + t ^ ) ∣ ∣ ADD = \frac{1}{m}\sum_{x_1\in M}\min \limits_{x_2\in M}||(Rx_1+t)-(\hat Rx_2+\hat t)|| ADD=m1x1∈M∑x2∈Mmin∣∣(Rx1+t)−(R^x2+t^)∣∣
4. Visible Surface Discrepancy (VSD)
ECCV 2016:On evaluation of 6d object pose estimation
paper 地址2
PPT
github
Visible Surface Discrepancy (VSD) is an ambiguityinvariant metric that depends on the visible surface, which can account for the symmetry better.
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