SARNet: Semantic Augmented Registration of Large-Scale Urban Point Clouds

• URL: http://arxiv.org/abs/2206.13117v2
• Date: Sun, 8 Oct 2023 12:31:28 GMT
• Title: SARNet: Semantic Augmented Registration of Large-Scale Urban Point Clouds
• Authors: Chao Liu, Jianwei Guo, Dong-Ming Yan, Zhirong Liang, Xiaopeng Zhang, Zhanglin Cheng
• Abstract summary: We propose SARNet, a novel semantic augmented registration network for urban point clouds. Our approach fully exploits semantic features as assistance to improve registration accuracy. We evaluate the proposed SARNet extensively by using real-world data from large regions of urban scenes.
• Score: 19.41446935340719
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Registering urban point clouds is a quite challenging task due to the large-scale, noise and data incompleteness of LiDAR scanning data. In this paper, we propose SARNet, a novel semantic augmented registration network aimed at achieving efficient registration of urban point clouds at city scale. Different from previous methods that construct correspondences only in the point-level space, our approach fully exploits semantic features as assistance to improve registration accuracy. Specifically, we extract per-point semantic labels with advanced semantic segmentation networks and build a prior semantic part-to-part correspondence. Then we incorporate the semantic information into a learning-based registration pipeline, consisting of three core modules: a semantic-based farthest point sampling module to efficiently filter out outliers and dynamic objects; a semantic-augmented feature extraction module for learning more discriminative point descriptors; a semantic-refined transformation estimation module that utilizes prior semantic matching as a mask to refine point correspondences by reducing false matching for better convergence. We evaluate the proposed SARNet extensively by using real-world data from large regions of urban scenes and comparing it with alternative methods. The code is available at https://github.com/WinterCodeForEverything/SARNet.

Related papers

• Deep Semantic Graph Matching for Large-scale Outdoor Point Clouds Registration [22.308070598885532]
  We treat the point cloud registration problem as a semantic instance matching and registration task. We propose a deep semantic graph matching method (DeepSGM) for large-scale outdoor point cloud registration. Experimental results conducted on the KITTI Odometry dataset demonstrate that the proposed method improves the registration performance.
  arXiv  Detail & Related papers  (2023-08-10T03:07:28Z)
• Human Semantic Segmentation using Millimeter-Wave Radar Sparse Point Clouds [3.3888257250564364]
  This paper presents a framework for semantic segmentation on sparse sequential point clouds of millimeter-wave radar. The sparsity and capturing temporal-topological features of mmWave data is still a problem. We introduce graph structure and topological features to the point cloud and propose a semantic segmentation framework. Our model achieves mean accuracy on a custom dataset by $mathbf82.31%$ and outperforms state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2023-04-27T12:28:06Z)
• CloudAttention: Efficient Multi-Scale Attention Scheme For 3D Point Cloud Learning [81.85951026033787]
  We set transformers in this work and incorporate them into a hierarchical framework for shape classification and part and scene segmentation. We also compute efficient and dynamic global cross attentions by leveraging sampling and grouping at each iteration. The proposed hierarchical model achieves state-of-the-art shape classification in mean accuracy and yields results on par with the previous segmentation methods.
  arXiv  Detail & Related papers  (2022-07-31T21:39:15Z)
• Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation [94.16816278191477]
  We present a framework for semi-adaptive and domain-supervised semantic segmentation. It is enhanced by self-supervised monocular depth estimation trained only on unlabeled image sequences. We validate the proposed model on the Cityscapes dataset.
  arXiv  Detail & Related papers  (2021-08-28T01:33:38Z)
• UPDesc: Unsupervised Point Descriptor Learning for Robust Registration [54.95201961399334]
  UPDesc is an unsupervised method to learn point descriptors for robust point cloud registration. We show that our learned descriptors yield superior performance over existing unsupervised methods.
  arXiv  Detail & Related papers  (2021-08-05T17:11:08Z)
• Point Cloud Registration using Representative Overlapping Points [10.843159482657303]
  We propose ROPNet, a new deep learning model using Representative Overlapping Points with discriminative features for registration. Specifically, we propose a context-guided module which uses an encoder to extract global features for predicting point overlap score. Experiments over ModelNet40 using noisy and partially overlapping point clouds show that the proposed method outperforms traditional and learning-based methods.
  arXiv  Detail & Related papers  (2021-07-06T12:52:22Z)
• Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling [52.464516118826765]
  We introduce RandLA-Net, an efficient and lightweight neural architecture to infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Our RandLA-Net can process 1 million points in a single pass up to 200x faster than existing approaches.
  arXiv  Detail & Related papers  (2021-07-06T05:08:34Z)
• S3Net: 3D LiDAR Sparse Semantic Segmentation Network [1.330528227599978]
  S3Net is a novel convolutional neural network for LiDAR point cloud semantic segmentation. It adopts an encoder-decoder backbone that consists of Sparse Intra-channel Attention Module (SIntraAM) and Sparse Inter-channel Attention Module (SInterAM)
  arXiv  Detail & Related papers  (2021-03-15T22:15:24Z)
• Towards Efficient Scene Understanding via Squeeze Reasoning [71.1139549949694]
  We propose a novel framework called Squeeze Reasoning. Instead of propagating information on the spatial map, we first learn to squeeze the input feature into a channel-wise global vector. We show that our approach can be modularized as an end-to-end trained block and can be easily plugged into existing networks.
  arXiv  Detail & Related papers  (2020-11-06T12:17:01Z)
• Spatial Pyramid Based Graph Reasoning for Semantic Segmentation [67.47159595239798]
  We apply graph convolution into the semantic segmentation task and propose an improved Laplacian. The graph reasoning is directly performed in the original feature space organized as a spatial pyramid. We achieve comparable performance with advantages in computational and memory overhead.
  arXiv  Detail & Related papers  (2020-03-23T12:28:07Z)

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.