GS-Matching: Reconsidering Feature Matching task in Point Cloud Registration

• URL: http://arxiv.org/abs/2412.04855v1
• Date: Fri, 06 Dec 2024 08:47:14 GMT
• Title: GS-Matching: Reconsidering Feature Matching task in Point Cloud Registration
• Authors: Yaojie Zhang, Tianlun Huang, Weijun Wang, Wei Feng,
• Abstract summary: We propose a stable matching policy called GS-matching, inspired by the Gale-Shapley algorithm. Our method can perform efficiently and find more non-repetitive inliers under low overlapping conditions.
• Score: 7.315456136190114
• License:
• Abstract: Traditional point cloud registration (PCR) methods for feature matching often employ the nearest neighbor policy. This leads to many-to-one matches and numerous potential inliers without any corresponding point. Recently, some approaches have framed the feature matching task as an assignment problem to achieve optimal one-to-one matches. We argue that the transition to the Assignment problem is not reliable for general correspondence-based PCR. In this paper, we propose a heuristics stable matching policy called GS-matching, inspired by the Gale-Shapley algorithm. Compared to the other matching policies, our method can perform efficiently and find more non-repetitive inliers under low overlapping conditions. Furthermore, we employ the probability theory to analyze the feature matching task, providing new insights into this research problem. Extensive experiments validate the effectiveness of our matching policy, achieving better registration recall on multiple datasets.

Related papers

• Semisupervised score based matching algorithm to evaluate the effect of public health interventions [3.221788913179251]
  In one-to-one matching algorithms, a large number of "pairs" to be matched could mean both the information from a large sample and a large number of tasks. We propose a novel one-to-one matching algorithm based on a quadratic score function $S_beta(x_i,x_j)= betaT (x_i-x_j)(x_i-x_j)T beta$.
  arXiv  Detail & Related papers  (2024-03-19T02:24:16Z)
• Query-Efficient Correlation Clustering with Noisy Oracle [17.11782578276788]
  We introduce two novel formulations of online learning problems rooted in the paradigm of Pure Exploration in Combinatorial Multi-Armed Bandits (PE-CMAB) We design algorithms that combine a sampling strategy with a classic approximation algorithm for correlation and study their theoretical guarantees. Our results are the first examples of clustering-time algorithms that work for the case of PE-CMAB in which the underlying offline optimization problem is NP-hard.
  arXiv  Detail & Related papers  (2024-02-02T13:31:24Z)
• Deep Probabilistic Graph Matching [72.6690550634166]
  We propose a deep learning-based graph matching framework that works for the original QAP without compromising on the matching constraints. The proposed method is evaluated on three popularly tested benchmarks (Pascal VOC, Willow Object and SPair-71k) and it outperforms all previous state-of-the-arts on all benchmarks.
  arXiv  Detail & Related papers  (2022-01-05T13:37:27Z)
• Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons [85.5955376526419]
  In rank aggregation problems, users exhibit various accuracy levels when comparing pairs of items. We propose an elimination-based active sampling strategy, which estimates the ranking of items via noisy pairwise comparisons. We prove that our algorithm can return the true ranking of items with high probability.
  arXiv  Detail & Related papers  (2021-10-08T13:51:55Z)
• Local policy search with Bayesian optimization [73.0364959221845]
  Reinforcement learning aims to find an optimal policy by interaction with an environment. Policy gradients for local search are often obtained from random perturbations. We develop an algorithm utilizing a probabilistic model of the objective function and its gradient.
  arXiv  Detail & Related papers  (2021-06-22T16:07:02Z)
• RANSIC: Fast and Highly Robust Estimation for Rotation Search and Point Cloud Registration using Invariant Compatibility [6.8858952804978335]
  Correspondence-based rotation search and point cloud registration are fundamental problems in robotics and computer vision. We present RANSIC, a fast and highly robust method applicable to both problems based on a new paradigm combining random sampling with invariance and compatibility. In multiple synthetic and real experiments, we demonstrate that RANSIC is fast for use, robust against over 95% outliers, and also able to recall approximately 100% inliers, outperforming other state-of-the-art solvers for both the rotation search and the point cloud registration problems.
  arXiv  Detail & Related papers  (2021-04-19T08:29:34Z)
• Consensus-Guided Correspondence Denoising [67.35345850146393]
  We propose to denoise correspondences with a local-to-global consensus learning framework to robustly identify correspondence. A novel "pruning" block is introduced to distill reliable candidates from initial matches according to their consensus scores estimated by dynamic graphs from local to global regions. Our method outperforms state-of-the-arts on robust line fitting, wide-baseline image matching and image localization benchmarks by noticeable margins.
  arXiv  Detail & Related papers  (2021-01-03T09:10:00Z)
• Optimal Off-Policy Evaluation from Multiple Logging Policies [77.62012545592233]
  We study off-policy evaluation from multiple logging policies, each generating a dataset of fixed size, i.e., stratified sampling. We find the OPE estimator for multiple loggers with minimum variance for any instance, i.e., the efficient one.
  arXiv  Detail & Related papers  (2020-10-21T13:43:48Z)
• Adaptive Sampling for Best Policy Identification in Markov Decision Processes [79.4957965474334]
  We investigate the problem of best-policy identification in discounted Markov Decision (MDPs) when the learner has access to a generative model. The advantages of state-of-the-art algorithms are discussed and illustrated.
  arXiv  Detail & Related papers  (2020-09-28T15:22:24Z)

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.