Incremental Rotation Averaging Revisited and More: A New Rotation Averaging Benchmark

• URL: http://arxiv.org/abs/2309.16924v3
• Date: Fri, 5 Jan 2024 02:49:43 GMT
• Title: Incremental Rotation Averaging Revisited and More: A New Rotation Averaging Benchmark
• Authors: Xiang Gao, Hainan Cui, and Shuhan Shen
• Abstract summary: A new member of the Incremental Rotation Averaging family is introduced, which is termed as IRAv4. A task-specific connected dominating set is extracted to serve as a more reliable and accurate reference for rotation global alignment. This paper presents a new COLMAP-based rotation averaging benchmark that incorporates a cross check between COLMAP and Bundler.
• Score: 19.315026204511973
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In order to further advance the accuracy and robustness of the incremental parameter estimation-based rotation averaging methods, in this paper, a new member of the Incremental Rotation Averaging (IRA) family is introduced, which is termed as IRAv4. As the most significant feature of the IRAv4, a task-specific connected dominating set is extracted to serve as a more reliable and accurate reference for rotation global alignment. In addition, to further address the limitations of the existing rotation averaging benchmark of relying on the slightly outdated Bundler camera calibration results as ground truths and focusing solely on rotation estimation accuracy, this paper presents a new COLMAP-based rotation averaging benchmark that incorporates a cross check between COLMAP and Bundler, and employ the accuracy of both rotation and downstream location estimation as evaluation metrics, which is desired to provide a more reliable and comprehensive evaluation tool for the rotation averaging research. Comprehensive comparisons between the proposed IRAv4 and other mainstream rotation averaging methods on this new benchmark demonstrate the effectiveness of our proposed approach.

Related papers

• Consensus-Adaptive RANSAC [104.87576373187426]
  We propose a new RANSAC framework that learns to explore the parameter space by considering the residuals seen so far via a novel attention layer. The attention mechanism operates on a batch of point-to-model residuals, and updates a per-point estimation state to take into account the consensus found through a lightweight one-step transformer.
  arXiv  Detail & Related papers  (2023-07-26T08:25:46Z)
• Revisiting Rotation Averaging: Uncertainties and Robust Losses [51.64986160468128]
  We argue that the main problem of current methods is the minimized cost function that is only weakly connected with the input data via the estimated epipolar. We propose to better model the underlying noise distributions by directly propagating the uncertainty from the point correspondences into the rotation averaging.
  arXiv  Detail & Related papers  (2023-03-09T11:51:20Z)
• CRIN: Rotation-Invariant Point Cloud Analysis and Rotation Estimation via Centrifugal Reference Frame [60.24797081117877]
  We propose the CRIN, namely Centrifugal Rotation-Invariant Network. CRIN directly takes the coordinates of points as input and transforms local points into rotation-invariant representations. A continuous distribution for 3D rotations based on points is introduced.
  arXiv  Detail & Related papers  (2023-03-06T13:14:10Z)
• RAGO: Recurrent Graph Optimizer For Multiple Rotation Averaging [62.315673415889314]
  This paper proposes a deep recurrent Rotation Averaging Graph (RAGO) for Multiple Rotation Averaging (MRA) Our framework is a real-time learning-to-optimize rotation averaging graph with a tiny size deployed for real-world applications.
  arXiv  Detail & Related papers  (2022-12-14T13:19:40Z)
• ART-Point: Improving Rotation Robustness of Point Cloud Classifiers via Adversarial Rotation [89.47574181669903]
  In this study, we show that the rotation robustness of point cloud classifiers can also be acquired via adversarial training. Specifically, our proposed framework named ART-Point regards the rotation of the point cloud as an attack. We propose a fast one-step optimization to efficiently reach the final robust model.
  arXiv  Detail & Related papers  (2022-03-08T07:20:16Z)
• Rotation-Only Bundle Adjustment [20.02647320786556]
  We propose a novel method for estimating the global rotations of the cameras independently of their positions and the scene structure. We extend this idea to multiple views, thereby decoupling the rotation estimation from the translation and structure estimation. We perform extensive evaluations on both synthetic and real datasets, demonstrating consistent and significant gains in accuracy when used with the state-of-the-art rotation averaging method.
  arXiv  Detail & Related papers  (2020-11-23T20:57:11Z)
• Orientation Keypoints for 6D Human Pose Estimation [15.347102634852613]
  We introduce orientation keypoints, a novel approach for estimating the full position and rotation of skeletal joints. Inspired by how motion-capture systems use a set of point markers to estimate full bone rotations, our method uses virtual markers to generate sufficient information. The rotation predictions improve upon the best reported mean error for joint angles by 48% and achieves 93% accuracy across 15 bone rotations.
  arXiv  Detail & Related papers  (2020-09-10T15:15:12Z)
• Robust 6D Object Pose Estimation by Learning RGB-D Features [59.580366107770764]
  We propose a novel discrete-continuous formulation for rotation regression to resolve this local-optimum problem. We uniformly sample rotation anchors in SO(3), and predict a constrained deviation from each anchor to the target, as well as uncertainty scores for selecting the best prediction. Experiments on two benchmarks: LINEMOD and YCB-Video, show that the proposed method outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-02-29T06:24:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.