Finding Geometric Models by Clustering in the Consensus Space

• URL: http://arxiv.org/abs/2103.13875v2
• Date: Mon, 17 Apr 2023 14:05:15 GMT
• Title: Finding Geometric Models by Clustering in the Consensus Space
• Authors: Daniel Barath, Denys Rozumny, Ivan Eichhardt, Levente Hajder, Jiri Matas
• Abstract summary: We propose a new algorithm for finding an unknown number of geometric models, e.g., homographies. We present a number of applications where the use of multiple geometric models improves accuracy. These include pose estimation from multiple generalized homographies; trajectory estimation of fast-moving objects.
• Score: 61.65661010039768
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a new algorithm for finding an unknown number of geometric models, e.g., homographies. The problem is formalized as finding dominant model instances progressively without forming crisp point-to-model assignments. Dominant instances are found via a RANSAC-like sampling and a consolidation process driven by a model quality function considering previously proposed instances. New ones are found by clustering in the consensus space. This new formulation leads to a simple iterative algorithm with state-of-the-art accuracy while running in real-time on a number of vision problems - at least two orders of magnitude faster than the competitors on two-view motion estimation. Also, we propose a deterministic sampler reflecting the fact that real-world data tend to form spatially coherent structures. The sampler returns connected components in a progressively densified neighborhood-graph. We present a number of applications where the use of multiple geometric models improves accuracy. These include pose estimation from multiple generalized homographies; trajectory estimation of fast-moving objects; and we also propose a way of using multiple homographies in global SfM algorithms. Source code: https://github.com/danini/clustering-in-consensus-space.

Related papers

• STREAM: A Universal State-Space Model for Sparse Geometric Data [2.9483719973596303]
  Handling unstructured geometric data, such as point clouds or event-based vision, is a pressing challenge in the field of machine vision. We propose to encode geometric structure explicitly into the parameterization of a state-space model. Our model deploys the Mamba selective state-space model with a modified kernel to efficiently map sparse data to modern hardware.
  arXiv  Detail & Related papers  (2024-11-19T16:06:32Z)
• PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus [26.366299016589256]
  We present a real-time method for robust estimation of multiple instances of geometric models from noisy data. A neural network segments the input data into clusters representing potential model instances. We demonstrate state-of-the-art performance on these as well as multiple established datasets, with inference times as small as five milliseconds per image.
  arXiv  Detail & Related papers  (2024-01-26T14:54:56Z)
• Adaptively-weighted Integral Space for Fast Multiview Clustering [54.177846260063966]
  We propose an Adaptively-weighted Integral Space for Fast Multiview Clustering (AIMC) with nearly linear complexity. Specifically, view generation models are designed to reconstruct the view observations from the latent integral space. Experiments conducted on several realworld datasets confirm the superiority of the proposed AIMC method.
  arXiv  Detail & Related papers  (2022-08-25T05:47:39Z)
• T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs [0.0]
  In graph-structured data, structured sparsity and smoothness tend to cluster together. We propose a new prior for high dimensional parameters with graphical relations. We use it to detect structured sparsity and smoothness simultaneously.
  arXiv  Detail & Related papers  (2021-07-06T10:10:03Z)
• Conjugate Mixture Models for Clustering Multimodal Data [24.640116037967985]
  The problem of multimodal clustering arises whenever the data are gathered with several physically different sensors. We show that multimodal clustering can be addressed within a novel framework, namely conjugate mixture models.
  arXiv  Detail & Related papers  (2020-12-09T10:13:22Z)
• Model identification and local linear convergence of coordinate descent [74.87531444344381]
  We show that cyclic coordinate descent achieves model identification in finite time for a wide class of functions. We also prove explicit local linear convergence rates for coordinate descent.
  arXiv  Detail & Related papers  (2020-10-22T16:03:19Z)
• Making Affine Correspondences Work in Camera Geometry Computation [62.7633180470428]
  Local features provide region-to-region rather than point-to-point correspondences. We propose guidelines for effective use of region-to-region matches in the course of a full model estimation pipeline. Experiments show that affine solvers can achieve accuracy comparable to point-based solvers at faster run-times.
  arXiv  Detail & Related papers  (2020-07-20T12:07:48Z)
• Learning Gaussian Graphical Models via Multiplicative Weights [54.252053139374205]
  We adapt an algorithm of Klivans and Meka based on the method of multiplicative weight updates. The algorithm enjoys a sample complexity bound that is qualitatively similar to others in the literature. It has a low runtime $O(mp2)$ in the case of $m$ samples and $p$ nodes, and can trivially be implemented in an online manner.
  arXiv  Detail & Related papers  (2020-02-20T10:50:58Z)
• CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus [62.86856923633923]
  We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. In contrast to previous works, which resorted to hand-crafted search strategies for multiple model detection, we learn the search strategy from data. For self-supervised learning of the search, we evaluate the proposed algorithm on multi-homography estimation and demonstrate an accuracy that is superior to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-08T17:37:01Z)

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.