Incremental Multiview Point Cloud Registration with Two-stage Candidate Retrieval

• URL: http://arxiv.org/abs/2407.07525v1
• Date: Wed, 10 Jul 2024 10:24:28 GMT
• Title: Incremental Multiview Point Cloud Registration with Two-stage Candidate Retrieval
• Authors: Shiqi Li, Jihua Zhu, Yifan Xie, Mingchen Zhu,
• Abstract summary: Multiview point cloud registration serves as a cornerstone of various computer vision tasks. We propose an incremental multiview point cloud registration method that progressively registers all scans to a growing meta-shape.
• Score: 12.528821749262931
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multiview point cloud registration serves as a cornerstone of various computer vision tasks. Previous approaches typically adhere to a global paradigm, where a pose graph is initially constructed followed by motion synchronization to determine the absolute pose. However, this separated approach may not fully leverage the characteristics of multiview registration and might struggle with low-overlap scenarios. In this paper, we propose an incremental multiview point cloud registration method that progressively registers all scans to a growing meta-shape. To determine the incremental ordering, we employ a two-stage coarse-to-fine strategy for point cloud candidate retrieval. The first stage involves the coarse selection of scans based on neighbor fusion-enhanced global aggregation features, while the second stage further reranks candidates through geometric-based matching. Additionally, we apply a transformation averaging technique to mitigate accumulated errors during the registration process. Finally, we utilize a Reservoir sampling-based technique to address density variance issues while reducing computational load. Comprehensive experimental results across various benchmarks validate the effectiveness and generalization of our approach.

Related papers

• Incremental Multiview Point Cloud Registration [18.830104930321223]
  We propose an incremental pipeline to progressively align scans into a canonical coordinate system. For detector-free matchers, we incorporate a Track refinement process. Experiments demonstrate that the proposed framework outperforms existing multiview registration methods on three benchmark datasets.
  arXiv  Detail & Related papers  (2024-07-06T09:28:23Z)
• Multiway Point Cloud Mosaicking with Diffusion and Global Optimization [74.3802812773891]
  We introduce a novel framework for multiway point cloud mosaicking (named Wednesday) At the core of our approach is ODIN, a learned pairwise registration algorithm that identifies overlaps and refines attention scores. Tested on four diverse, large-scale datasets, our method state-of-the-art pairwise and rotation registration results by a large margin on all benchmarks.
  arXiv  Detail & Related papers  (2024-03-30T17:29:13Z)
• Point Cloud Pre-training with Diffusion Models [62.12279263217138]
  We propose a novel pre-training method called Point cloud Diffusion pre-training (PointDif) PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification, segmentation and detection.
  arXiv  Detail & Related papers  (2023-11-25T08:10:05Z)
• DealMVC: Dual Contrastive Calibration for Multi-view Clustering [78.54355167448614]
  We propose a novel Dual contrastive calibration network for Multi-View Clustering (DealMVC) We first design a fusion mechanism to obtain a global cross-view feature. Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph. During the training procedure, the interacted cross-view feature is jointly optimized at both local and global levels.
  arXiv  Detail & Related papers  (2023-08-17T14:14:28Z)
• Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap [27.362946585463824]
  Global point cloud registration is essential in many robotics tasks like loop closing and relocalization. Unfortunately, the registration often suffers from the low overlap between point clouds. In this paper, we propose a robust framework to address the problem of point registration with low overlap.
  arXiv  Detail & Related papers  (2023-07-22T16:05:23Z)
• HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion [15.94976936555104]
  We propose a cross-source point cloud fusion algorithm called HybridFusion. It can register cross-source dense point clouds from different viewing angle in outdoor large scenes. The proposed approach is evaluated comprehensively through qualitative and quantitative experiments.
  arXiv  Detail & Related papers  (2023-04-10T10:54:54Z)
• Category-Level Global Camera Pose Estimation with Multi-Hypothesis Point Cloud Correspondences [26.885846254261626]
  Correspondence search is an essential step in rigid point cloud registration algorithms. This paper proposes an optimization method that retains all possible correspondences for each keypoint when matching a partial point cloud to a complete point cloud. We also propose a new point feature descriptor that measures the similarity between local point cloud regions.
  arXiv  Detail & Related papers  (2022-09-28T21:12:51Z)
• BIMS-PU: Bi-Directional and Multi-Scale Point Cloud Upsampling [60.257912103351394]
  We develop a new point cloud upsampling pipeline called BIMS-PU. We decompose the up/downsampling procedure into several up/downsampling sub-steps by breaking the target sampling factor into smaller factors. We show that our method achieves superior results to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-06-25T13:13:37Z)
• Consistent Two-Flow Network for Tele-Registration of Point Clouds [74.51029406361997]
  We present a learning-based technique that allows registration between point clouds, presented in arbitrary poses, and having little or even no overlap. Our technique is based on a novel neural network design that learns a prior of a class of shapes and can complete a partial shape. We show that, compared with each separate flow, this two-flow training leads to robust and reliable tele-registration.
  arXiv  Detail & Related papers  (2021-06-01T09:03:21Z)
• Learning multiview 3D point cloud registration [74.39499501822682]
  We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm. Our approach outperforms the state-of-the-art by a significant margin, while being end-to-end trainable and computationally less costly.
  arXiv  Detail & Related papers  (2020-01-15T03:42:14Z)

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.