TriVoC: Efficient Voting-based Consensus Maximization for Robust Point Cloud Registration with Extreme Outlier Ratios

• URL: http://arxiv.org/abs/2111.00657v1
• Date: Mon, 1 Nov 2021 02:03:40 GMT
• Title: TriVoC: Efficient Voting-based Consensus Maximization for Robust Point Cloud Registration with Extreme Outlier Ratios
• Authors: Lei Sun, Lu Deng
• Abstract summary: We present a novel, fast, deterministic and guaranteed robust solver, named TriVoC, for the robust registration problem. We show that TriVoC is robust against up to 99% outliers, highly accurate, time-efficient even with extreme outlier ratios, and also practical for real-world applications.
• Score: 6.8858952804978335
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Correspondence-based point cloud registration is a cornerstone in robotics perception and computer vision, which seeks to estimate the best rigid transformation aligning two point clouds from the putative correspondences. However, due to the limited robustness of 3D keypoint matching approaches, outliers, probably in large numbers, are prone to exist among the correspondences, which makes robust registration methods imperative. Unfortunately, existing robust methods have their own limitations (e.g. high computational cost or limited robustness) when facing high or extreme outlier ratios, probably unsuitable for practical use. In this paper, we present a novel, fast, deterministic and guaranteed robust solver, named TriVoC (Triple-layered Voting with Consensus maximization), for the robust registration problem. We decompose the selecting of the minimal 3-point sets into 3 consecutive layers, and in each layer we design an efficient voting and correspondence sorting framework on the basis of the pairwise equal-length constraint. In this manner, the 3-point sets can be selected independently from the reduced correspondence sets according to the sorted sequence, which can significantly lower the computational cost and meanwhile provide a strong guarantee to achieve the largest consensus set (as the final inlier set) as long as a probabilistic termination condition is fulfilled. Varied experiments show that our solver TriVoC is robust against up to 99% outliers, highly accurate, time-efficient even with extreme outlier ratios, and also practical for real-world applications, showing performance superior to other state-of-the-art competitors.

Related papers

• Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentation [80.07161039753043]
  Weakly-supervised point cloud segmentation with extremely limited labels is desirable to alleviate the expensive costs of collecting densely annotated 3D points. This paper explores applying the consistency regularization that is commonly used in weakly-supervised learning, for its point cloud counterpart with multiple data-specific augmentations. We propose a novel Reliability-Adaptive Consistency Network (RAC-Net) to use both prediction confidence and model uncertainty to measure the reliability of pseudo labels.
  arXiv  Detail & Related papers  (2023-03-09T10:41:57Z)
• Practical, Fast and Robust Point Cloud Registration for 3D Scene Stitching and Object Localization [6.8858952804978335]
  3D point cloud registration is a fundamental problem in remote sensing, photogrammetry, robotics and geometric computer vision. We propose a novel, fast and highly robust solution, named VOCRA, for the point cloud registration problem with extreme outlier rates. We show that our solver VOCRA is robust against over 99% outliers and more time-efficient than the state-of-the-art competitors.
  arXiv  Detail & Related papers  (2021-11-08T01:49:04Z)
• DANIEL: A Fast and Robust Consensus Maximization Method for Point Cloud Registration with High Outlier Ratios [6.8858952804978335]
  Correspondence-based point cloud registration is a cornerstone in computer vision, robotics perception, photogrammetry and remote sensing. Current 3D keypoint matching techniques are very prone to yield outliers, probably even in very large numbers. We present a novel time-efficient RANSAC-type consensus solver, named DANIEL, for robust registration.
  arXiv  Detail & Related papers  (2021-10-11T08:27:00Z)
• Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination [82.52105963476703]
  A recurring theme in statistical learning, online learning, and beyond is that faster convergence rates are possible for problems with low noise. First-order guarantees are relatively well understood in statistical and online learning. We show that the logarithmic loss and an information-theoretic quantity called the triangular discrimination play a fundamental role in obtaining first-order guarantees.
  arXiv  Detail & Related papers  (2021-07-05T19:20:34Z)
• ICOS: Efficient and Highly Robust Point Cloud Registration with Correspondences [6.8858952804978335]
  Point Cloud Registration is a fundamental problem in robotics and computer vision. In this paper, we present ICOS, a novel, efficient and highly robust solution for the correspondence-based point cloud registration problem.
  arXiv  Detail & Related papers  (2021-04-30T05:41:53Z)
• IRON: Invariant-based Highly Robust Point Cloud Registration [6.8858952804978335]
  In this paper, we present a non-minimal and highly robust solution for point cloud registration with a great number of outliers. We show that IRON is efficient, highly accurate and robust against as many as 99% outliers whether the scale is known or unknown.
  arXiv  Detail & Related papers  (2021-03-07T13:46:56Z)
• Canny-VO: Visual Odometry with RGB-D Cameras based on Geometric 3D-2D Edge Alignment [85.32080531133799]
  This paper reviews the classical problem of free-form curve registration and applies it to an efficient RGBD visual odometry system called Canny-VO. Two replacements for the distance transformation commonly used in edge registration are proposed: Approximate Nearest Neighbour Fields and Oriented Nearest Neighbour Fields. 3D2D edge alignment benefits from these alternative formulations in terms of both efficiency and accuracy.
  arXiv  Detail & Related papers  (2020-12-15T11:42:17Z)
• On Efficient and Robust Metrics for RANSAC Hypotheses and 3D Rigid Registration [51.64236850960365]
  This paper focuses on developing efficient and robust evaluation metrics for RANSAC hypotheses to achieve accurate 3D rigid registration. We analyze the contributions of inliers and outliers, and then proposing several efficient and robust metrics with different designing motivations for RANSAC hypotheses.
  arXiv  Detail & Related papers  (2020-11-10T02:22:45Z)
• Learning 3D-3D Correspondences for One-shot Partial-to-partial Registration [66.41922513553367]
  We show that learning-based partial-to-partial registration can be achieved in a one-shot manner. We propose an Optimal Transport layer able to account for occluded points thanks to the use of bins. The resulting OPRNet framework outperforms the state of the art on standard benchmarks.
  arXiv  Detail & Related papers  (2020-06-08T12:35:47Z)
• Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference [119.19779637025444]
  Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) This paper studies multi-exit networks associated with input-adaptive inference, showing their strong promise in achieving a "sweet point" in cooptimizing model accuracy, robustness and efficiency.
  arXiv  Detail & Related papers  (2020-02-24T00:40:22Z)
• TEASER: Fast and Certifiable Point Cloud Registration [30.19476775410544]
  First fast and robust certifiable algorithm for the registration of 3D points in the presence of large amounts of outliers. Second fast and robust certifiable translation, named TEASER++, uses graduated non-componentity to solve a large subproblem.
  arXiv  Detail & Related papers  (2020-01-21T18:56:00Z)

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.