| 1. |
- Jorge, Emilio, 1992, et al.
(författare)
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Reinforcement learning in real-time geometry assurance
- 2018
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Ingår i: Procedia CIRP. - : Elsevier BV. - 2212-8271. ; 72, s. 1073-1078
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Konferensbidrag (refereegranskat)abstract
- To improve the assembly quality during production, expert systems are often used. These experts typically use a system model as a basis for identifying improvements. However, since a model uses approximate dynamics or imperfect parameters, the expert advice is bound to be biased. This paper presents a reinforcement learning agent that can identify and limit systematic errors of an expert systems used for geometry assurance. By observing the resulting assembly quality over time, and understanding how different decisions affect the quality, the agent learns when and how to override the biased advice from the expert software.
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| 2. |
- Toft, Carl, 1990, et al.
(författare)
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Semantic Match Consistency for Long-Term Visual Localization
- 2018
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 11206 LNCS, s. 391-408
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Konferensbidrag (refereegranskat)abstract
- Robust and accurate visual localization across large appearance variations due to changes in time of day, seasons, or changes of the environment is a challenging problem which is of importance to application areas such as navigation of autonomous robots. Traditional feature-based methods often struggle in these conditions due to the significant number of erroneous matches between the image and the 3D model. In this paper, we present a method for scoring the individual correspondences by exploiting semantic information about the query image and the scene. In this way, erroneous correspondences tend to get a low semantic consistency score, whereas correct correspondences tend to get a high score. By incorporating this information in a standard localization pipeline, we show that the localization performance can be significantly improved compared to the state-of-the-art, as evaluated on two challenging long-term localization benchmarks.
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| 3. |
- Brynte, Lucas, 1990
(författare)
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Learning and Optimizing Camera Pose
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Plenty of computer vision applications involve assessing the position and orientation, i.e. the pose , of one or several cameras, including object pose estimation, visual localization, and structure-from-motion. Traditionally, such problems have often been addressed by detection, extraction, and matching of image keypoints, using handcrafted local image features such as the scale-invariant feature transform (SIFT), followed by robust fitting and / or optimization to determine the unknown camera pose(s). Learning-based models have the advantage that the they can learn from data what cues or patterns are relevant for the task, beyond the imagination of the engineer. However, compared with 2D vision tasks such as image classification and object detection, applying machine learning models to 3D vision tasks such as pose estimation has proven to be more challenging. In this thesis, I explore pose estimation methods based on machine learning and optimization, from the aspects of quality, robustness, and efficiency. First, an efficient and powerful graph attention network model for learning structure-from-motion is presented, taking image point tracks as input. Generalization capabilities to novel scenes is then demonstrated, without costly fine-tuning of network parameters. Combined with bundle adjustment, accurate reconstructions are acquired, significantly faster than off-the-shelf incremental structure-from-motion pipelines. Second, techniques are presented for improving the equivariance properties of convolutional neural network models carrying out pose estimation, either by intentionally applying radial distortion to images to reduce perspective effects, or via a geometrically sound data augmentation scheme corresponding to camera motion. Next, the power and limitations of semidefinite relaxations of pose optimization problems are explored, notably leading to the conclusion that absolute camera pose estimation is not necessarily solvable using the considered semidefinite relaxations, since while they tend to almost always be tight in practice, counter-examples do indeed exist. Finally, a rendering-based object pose refinement method is presented, robust to partial occlusion due to its implicit nature, followed by a method for long-term visual localization, leveraging on a semantic segmentation model to increase the robustness by promoting semantic consistency of sampled point correspondences.
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| 4. |
- Brynte, Lucas, 1990, et al.
(författare)
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On the Tightness of Semidefinite Relaxations for Rotation Estimation
- 2022
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Ingår i: Journal of Mathematical Imaging and Vision. - : Springer Science and Business Media LLC. - 1573-7683 .- 0924-9907. ; 64:1, s. 57-67
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Tidskriftsartikel (refereegranskat)abstract
- Why is it that semidefinite relaxations have been so successful in numerous applications in computer vision and robotics for solving non-convex optimization problems involving rotations? In studying the empirical performance, we note that there are few failure cases reported in the literature, in particular for estimation problems with a single rotation, motivating us to gain further theoretical understanding. A general framework based on tools from algebraic geometry is introduced for analyzing the power of semidefinite relaxations of problems with quadratic objective functions and rotational constraints. Applications include registration, hand–eye calibration, and rotation averaging. We characterize the extreme points and show that there exist failure cases for which the relaxation is not tight, even in the case of a single rotation. We also show that some problem classes are always tight given an appropriate parametrization. Our theoretical findings are accompanied with numerical simulations, providing further evidence and understanding of the results.
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| 5. |
- Brynte, Lucas, 1990, et al.
(författare)
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Pose Proposal Critic: Robust Pose Refinement by Learning Reprojection Errors
- 2020
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Ingår i: 31st British Machine Vision Conference, BMVC 2020.
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Konferensbidrag (refereegranskat)abstract
- In recent years, considerable progress has been made for the task of rigid object pose estimation from a single RGB-image, but achieving robustness to partial occlusions remains a challenging problem. Pose refinement via rendering has shown promise in order to achieve improved results, in particular, when data is scarce. In this paper we focus our attention on pose refinement, and show how to push the state-of-the-art further in the case of partial occlusions. The proposed pose refinement method leverages on a simplified learning task, where a CNN is trained to estimate the reprojection error between an observed and a rendered image. We experiment by training on purely synthetic data as well as a mixture of synthetic and real data. Current state-of-the-art results are outperformed for two out of three metrics on the Occlusion LINEMOD benchmark, while performing on-par for the final metric.
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| 6. |
- Brynte, Lucas, 1990, et al.
(författare)
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Rigidity preserving image transformations and equivariance in perspective
- 2023
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Ingår i: Image analysis. - Cham : Springer Nature. - 9783031314384 - 9783031314377 ; 13886 LNCS, s. 59-76
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Konferensbidrag (refereegranskat)abstract
- We characterize the class of image plane transformations which realize rigid camera motions and call these transformations ‘rigidity preserving’. It turns out that the only rigidity preserving image transformations are homographies corresponding to rotating the camera. In particular, 2D translations of pinhole images are not rigidity preserving. Hence, when using CNNs for 3D inference tasks, it can be beneficial to modify the inductive bias from equivariance w.r.t. translations to equivariance w.r.t. rotational homographies. We investigate how equivariance with respect to rotational homographies can be approximated in CNNs, and test our ideas on 6D object pose estimation. Experimentally, we improve on a competitive baseline.
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