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Object Pose Estimation
Arithmer R3 Div. R3 - Ryosuke Sasaki
• Correspondence-based method : Find Correspondences between 2D points and 3D points (PnP, ICP)
• Template-based method: Extract the gradient information for matching, and refined(ICP)
• Voting-based method: Every local predicts a result, and refined using RANSAC
• Regression-based method: Represent pose suitable for CNN
Arxiv:1905.06658
Arxiv:1905.06658
Correspondence-based method
With rich textures to get image features
Arxiv:1905.06658
PnP solver
Estimate extrinsic camera parameter using known 3D points in a world coordinate
There are many many solvers (e.g. P3P, P5P.., using RANSAC or Eigen value decomposition)
ICP algorithm
Iterative matching for 2 point clouds
1. Correspondence nearest neighbor points
2. Translate a point cloud set to match the other sets
3. iteration above
Arxiv:1905.06658
ICP
Estimate extrinsic camera parameter using known 3D points in a world coordinate
There are many many solvers (e.g. P3P, P5P.., using RANSAC or Eigen value decomposition)
SegICP
Segmentation and depth image-based partial
Registration to obtain object’s pose
SegNet
Multi-hypothesis object pose:
Point-to-point ICP
1cm pose error and < 5°error
Arxiv:1905.06658
Template-based method
Using gradient information in RGB-D or RGB image
Without rich texture
ICP
Arxiv:1905.06658
Voting-based method
Objects with occlusions
Dense class labeling
Regression-based method
PoseCNN
- Localizing the center of objects
- Predicting its distance from camera
- Regressing rotation to a quarternion
Regression-based method
PoseCNN architecture
- Feature extraction
- Embedding
- Classification and regression
Two loss function for quaternion
- PoseLoss
- ShapeMatch-Loss
Regression-based method
DeepIM
Iterative refinement using DNN
Enable to combine other estimator
Regression-based method
DeepIM
Iterative refinement using DNN
Flownet: estimate optical flow
Regression-based method
CullNet
Single view image-based object pose estimation
Culling false positives
Calibrate the confidence score
Correspondence-based and Regression-based method
CullNet Architecture
Using Yolov3 for keypoints
Cullnet output confidence
of objects pose by PnP.
Voting-based and Regression-based method
DenseFusion
Estimating 6D pose of known objects
Comparison
ADD (Average Distance of Model Point)
ADD-S (for symmetric objects
Error smaller than threshold(< 2cm)
From the tables, we can see that DenseFusion
achieved the highest accuracy comparing with other
regression-based methods.
novel local feature fusion scheme using both RGB
and depth images
19

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