DeepIM: Deep Iterative Matching for 6D Pose Estimation
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Direct pose regression methods aim to fit a mapping function from 2D image observations to 3D pose or pose updates.
“Although the architectures varied, all the above methods aim to directly fit a mapping function from 2D image observations to 3D pose or pose updates, taking deep networks as powerful encoders.”
From A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers (2024) · cites this paper as [19]
Learning from only a 2D domain without 3D parameters limits the network ability, likely to overfit the training domain.
“Learning from only a 2D domain without 3D parameters limits the network ability, likely to overfit the training domain.”
From A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers (2024) · cites this paper as [19]
DeepIM performs iterative pose updates using learned gradient predictions.
“For example, Li et al. proposed DeepIM, which performs iterative pose updates using learned gradient predictions [19].”
From A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers (2024) · cites this paper as [19]
Learning-based iterative 2D/3D registration methods that do not optimize a cost function have no guarantee of convergence to global or local minima.
“Since the registration does not optimize a cost function, but the iterative pose prediction is extracted from 2D images, there is no guarantee for convergence to global or local minima.”
From A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers (2024) · cites this paper as [19]
Current learning-based iterative 2D/3D registration methods predict a pose update from 2D image observations by either framing it as a reinforcement learning task or direct gradient update regression.
“The current learning-based iterative 2D/3D registration methods in literature aim at predicting a pose update from 2D image observations by either framing it as a reinforcement learning task or direct gradient update regression.”
From A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers (2024) · cites this paper as [19]
Pose regression-based 2D/3D registration approaches are performed with respect to a canonical coordinate system derived from an implicit atlas, which is challenging to be precisely defined.
“Pose regression-based 2D/3D registration approaches are performed with respect to a canonical coordinate system derived from an implicit atlas, which is challenging to be precisely defined.”
From A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers (2024) · cites this paper as [19]
For learning-based iterative 2D/3D registration methods, the prediction can change drastically if the input image's appearance alters by a small change.
“The prediction can change drastically if the input image's appearance alters by a small change.”
From A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers (2024) · cites this paper as [19]
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FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects
Wen, B., Yang, W., Kautz, J., & Birchfield, S. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01692 · Cited by 353 · see citing works
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Neural Network-Based Pose Estimation for Noncooperative Spacecraft Rendezvous
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A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers
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- Authors
- Li, Y., Wang, G., Ji, X., Xiang, Y., & Fox, D.
- Affiliations
- Tsinghua University (CN)
- University of Washington (US)
- Corresponding author
- Li, Yi
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 695-711
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2962783853
- Cited by
- 576
- References
- 11
- Citations by year
- 2026: 29 · 2025: 60 · 2024: 89 · 2023: 77 · 2022: 86 · 2021: 99 · 2020: 95 · 2019: 39 · 2018: 2
- FWCI
- 88.20
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- 6D pose estimation, unseen object generalization, robotic manipulation
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