Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap

• URL: http://arxiv.org/abs/2307.12116v1
• Date: Sat, 22 Jul 2023 16:05:23 GMT
• Title: Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap
• Authors: Zhijian Qiao, Zehuan Yu, Huan Yin and Shaojie Shen
• Abstract summary: 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.
• Score: 27.362946585463824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, a frequent occurrence in practical applications due to occlusion and viewpoint change. In this paper, we propose a graph-theoretic framework to address the problem of global point cloud registration with low overlap. To this end, we construct a consistency graph to facilitate robust data association and employ graduated non-convexity (GNC) for reliable pose estimation, following the state-of-the-art (SoTA) methods. Unlike previous approaches, we use semantic cues to scale down the dense point clouds, thus reducing the problem size. Moreover, we address the ambiguity arising from the consistency threshold by constructing a pyramid graph with multi-level consistency thresholds. Then we propose a cascaded gradient ascend method to solve the resulting densest clique problem and obtain multiple pose candidates for every consistency threshold. Finally, fast geometric verification is employed to select the optimal estimation from multiple pose candidates. Our experiments, conducted on a self-collected indoor dataset and the public KITTI dataset, demonstrate that our method achieves the highest success rate despite the low overlap of point clouds and low semantic quality. We have open-sourced our code https://github.com/HKUST-Aerial-Robotics/Pagor for this project.

Related papers

• 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)
• Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding [51.75091298017941]
  This paper proposes a novel Deep Manifold (Variational) Graph Auto-Encoder (DMVGAE/DMGAE) for attributed graph data. The proposed method surpasses state-of-the-art baseline algorithms by a significant margin on different downstream tasks across popular datasets.
  arXiv  Detail & Related papers  (2024-01-12T17:57:07Z)
• Learning Continuous Implicit Field with Local Distance Indicator for Arbitrary-Scale Point Cloud Upsampling [55.05706827963042]
  Point cloud upsampling aims to generate dense and uniformly distributed point sets from a sparse point cloud. Previous methods typically split a sparse point cloud into several local patches, upsample patch points, and merge all upsampled patches. We propose a novel approach that learns an unsigned distance field guided by local priors for point cloud upsampling.
  arXiv  Detail & Related papers  (2023-12-23T01:52:14Z)
• Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment [50.892158511845466]
  We show that gradient consistency in the field, indicated by the parallelism of level sets, is the key factor affecting the inference accuracy. We propose a level set alignment loss to evaluate the parallelism of level sets, which can be minimized to achieve better gradient consistency.
  arXiv  Detail & Related papers  (2023-05-19T11:28:05Z)
• Learnable Graph Matching: A Practical Paradigm for Data Association [74.28753343714858]
  We propose a general learnable graph matching method to address these issues. Our method achieves state-of-the-art performance on several MOT datasets. For image matching, our method outperforms state-of-the-art methods on a popular indoor dataset, ScanNet.
  arXiv  Detail & Related papers  (2023-03-27T17:39:00Z)
• Robust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version) [13.286247750893681]
  3D point cloud registration is a fundamental problem in computer vision and robotics. We propose a novel deep graph matching-based framework for point cloud registration.
  arXiv  Detail & Related papers  (2022-11-09T06:05:25Z)
• Fast and Robust Registration of Partially Overlapping Point Clouds [5.073765501263891]
  Real-time registration of partially overlapping point clouds has emerging applications in cooperative perception for autonomous vehicles. Relative translation between point clouds in these applications is higher than in traditional SLAM and odometry applications. We propose a novel registration method for partially overlapping point clouds where correspondences are learned using an efficient point-wise feature encoder.
  arXiv  Detail & Related papers  (2021-12-18T12:39:05Z)
• Robust Point Cloud Registration Framework Based on Deep Graph Matching [5.865029600972316]
  3D point cloud registration is a fundamental problem in computer vision and robotics. We propose a novel deep graph matchingbased framework for point cloud registration.
  arXiv  Detail & Related papers  (2021-03-07T04:20:29Z)
• Semantic Graph Based Place Recognition for 3D Point Clouds [22.608115489674653]
  This paper presents a novel semantic graph based approach for place recognition. First, we propose a novel semantic graph representation for the point cloud scenes. We then design a fast and effective graph similarity network to compute the similarity.
  arXiv  Detail & Related papers  (2020-08-26T09:27:26Z)
• DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registration [12.471564670462344]
  This work addresses the problem of point cloud registration using deep neural networks. We propose an approach to predict the alignment between two point clouds with overlapping data content, but displaced origins. Our approach achieves state-of-the-art accuracy and the lowest run-time of the compared methods.
  arXiv  Detail & Related papers  (2020-07-22T08:20:57Z)
• 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.