CroCPS: Addressing Photometric Challenges in Self-Supervised Category-Level 6D Object Poses with Cross-Modal Learning
@inproceedings{Wang2022CroCPSAP, title={CroCPS: Addressing Photometric Challenges in Self-Supervised Category-Level 6D Object Poses with Cross-Modal Learning}, author={Pengyuan Wang and Lorenzo Garattoni and Sven Meier and Nassir Navab and Benjamin Busam}, booktitle={British Machine Vision Conference}, year={2022}, url={https://api.semanticscholar.org/CorpusID:256903232} }
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39 References
Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation
- 2024
Computer Science
This work proposes a novel monocular 6D pose estimation approach by means of self-supervised learning, removing the need for real annotations, and demonstrates that this proposed self-supervision outperforms all other methods relying on synthetic data or employing elaborate techniques from the domain adaptation realm.
CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning
- 2020
Computer Science, Engineering
This work proposes a novel method for class-level monocular 6D pose estimation, coupled with metric shape retrieval, and leverages recent advances in differentiable rendering to self-supervise the model with unannotated real RGB-D data to improve latter inference.
Self6D: Self-Supervised Monocular 6D Object Pose Estimation
- 2020
Computer Science
This work proposes the idea of monocular 6D pose estimation by means of self-supervised learning, removing the need for real annotations, and demonstrates that the proposed self- supervision model is able to significantly enhance the model's original performance, outperforming all other methods relying on synthetic data or employing elaborate techniques from the domain adaptation realm.
PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects
- 2022
Computer Science, Engineering
A novel robot-supported multi-modal (RGB, depth, polarisation) data acquisition and annotation process is developed that ensures sub-millimeter accuracy of the pose for opaque textured, shiny and transparent objects, no motion blur and perfect camera synchronisation.
WS-OPE: Weakly Supervised 6D Object Pose Regression using Relative Multi-Camera Pose Constraints
- 2022
Computer Science
The proposed scalable, end-to-end 6D pose regression with weak supervision without the need for a consecutive refinement stage thereby ensures real-time performance and is capable of predicting poses of good quality, in spite being trained with only weak labels.
Self-Supervised Category-Level 6D Object Pose Estimation with Deep Implicit Shape Representation
- 2022
Computer Science, Engineering
A self-supervised framework for category-level 6D pose estimation that leverages DeepSDF as a 3D object representation and design several novel loss functions based onDeepSDF to help the self- supervised model predict unseen object poses without any 6D object pose labels and explicit 3D models in real scenarios.
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
- 2019
Computer Science, Engineering
DenseFusion is a generic framework for estimating 6D pose of a set of known objects from RGB-D images that processes the two data sources individually and uses a novel dense fusion network to extract pixel-wise dense feature embedding, from which the pose is estimated.
UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation
- 2022
Computer Science
This work proposes an unsupervised domain adaptation (UDA) for category-level object pose estimation, called UDA-COPE, which exploits a teacher-student self-supervised learning scheme to train a pose estimation network without using target domain pose labels.
CroMo: Cross-Modal Learning for Monocular Depth Estimation
- 2022
Computer Science, Engineering
This paper proposes a novel pipeline capable of estimating depth from monocular polarisation, and proposes the inversion of differentiable analytic models thereby connects scene geometry with polarisation and ToF signals and enables self-supervised and cross-modal learning.
NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation
- 2023
Computer Science
A weakly-supervised reconstruction-based pipeline, named NeRF-Pose, which needs only 2D bounding boxes and relative camera poses during training and has state-of-the-art accuracy in comparison to the best 6D pose estimation methods in spite of being trained only with weak labels.