GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentation

• URL: http://arxiv.org/abs/2207.02605v1
• Date: Wed, 6 Jul 2022 11:48:08 GMT
• Title: GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentation
• Authors: Haibo Qiu, Baosheng Yu and Dacheng Tao
• Abstract summary: We introduce a geometric flow network (GFNet) to explore the geometric correspondence between different views in an align-before-fuse manner. Specifically, we devise a novel geometric flow module (GFM) to bidirectionally align and propagate the complementary information across different views.
• Score: 91.15865862160088
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Point cloud semantic segmentation from projected views, such as range-view (RV) and bird's-eye-view (BEV), has been intensively investigated. Different views capture different information of point clouds and thus are complementary to each other. However, recent projection-based methods for point cloud semantic segmentation usually utilize a vanilla late fusion strategy for the predictions of different views, failing to explore the complementary information from a geometric perspective during the representation learning. In this paper, we introduce a geometric flow network (GFNet) to explore the geometric correspondence between different views in an align-before-fuse manner. Specifically, we devise a novel geometric flow module (GFM) to bidirectionally align and propagate the complementary information across different views according to geometric relationships under the end-to-end learning scheme. We perform extensive experiments on two widely used benchmark datasets, SemanticKITTI and nuScenes, to demonstrate the effectiveness of our GFNet for project-based point cloud semantic segmentation. Concretely, GFNet not only significantly boosts the performance of each individual view but also achieves state-of-the-art results over all existing projection-based models. Code is available at \url{https://github.com/haibo-qiu/GFNet}.

Related papers

• CP-VoteNet: Contrastive Prototypical VoteNet for Few-Shot Point Cloud Object Detection [7.205000222081269]
  Few-shot point cloud 3D object detection (FS3D) aims to identify and localise objects of novel classes from point clouds. We introduce contrastive semantics mining, which enables the network to extract discriminative categorical features. Through refined primitive geometric structures, the transferability of feature encoding from base to novel classes is significantly enhanced.
  arXiv  Detail & Related papers  (2024-08-30T06:13:49Z)
• Self-supervised 3D Point Cloud Completion via Multi-view Adversarial Learning [61.14132533712537]
  We propose MAL-SPC, a framework that effectively leverages both object-level and category-specific geometric similarities to complete missing structures. Our MAL-SPC does not require any 3D complete supervision and only necessitates a single partial point cloud for each object.
  arXiv  Detail & Related papers  (2024-07-13T06:53:39Z)
• Geometrically-driven Aggregation for Zero-shot 3D Point Cloud Understanding [11.416392706435415]
  Zero-shot 3D point cloud understanding can be achieved via 2D Vision-Language Models (VLMs) Existing strategies directly map Vision-Language Models from 2D pixels of rendered or captured views to 3D points, overlooking the inherent and expressible point cloud geometric structure. We introduce the first training-free aggregation technique that leverages the point cloud's 3D geometric structure to improve the quality of the transferred Vision-Language Models.
  arXiv  Detail & Related papers  (2023-12-04T12:30:07Z)
• Generalized Few-Shot Point Cloud Segmentation Via Geometric Words [54.32239996417363]
  Few-shot point cloud segmentation algorithms learn to adapt to new classes at the sacrifice of segmentation accuracy for the base classes. We present the first attempt at a more practical paradigm of generalized few-shot point cloud segmentation. We propose the geometric words to represent geometric components shared between the base and novel classes, and incorporate them into a novel geometric-aware semantic representation.
  arXiv  Detail & Related papers  (2023-09-20T11:24:33Z)
• Adaptive Edge-to-Edge Interaction Learning for Point Cloud Analysis [118.30840667784206]
  Key issue for point cloud data processing is extracting useful information from local regions. Previous works ignore the relation between edges in local regions, which encodes the local shape information. This paper proposes a novel Adaptive Edge-to-Edge Interaction Learning module.
  arXiv  Detail & Related papers  (2022-11-20T07:10:14Z)
• Multi-scale Network with Attentional Multi-resolution Fusion for Point Cloud Semantic Segmentation [2.964101313270572]
  We present a comprehensive point cloud semantic segmentation network that aggregates both local and global multi-scale information. We introduce an Angle Correlation Point Convolution module to effectively learn the local shapes of points. Third, inspired by HRNet which has excellent performance on 2D image vision tasks, we build an HRNet customized for point cloud to learn global multi-scale context.
  arXiv  Detail & Related papers  (2022-06-27T21:03:33Z)
• SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation [94.11915008006483]
  We propose SemAffiNet for point cloud semantic segmentation. We conduct extensive experiments on the ScanNetV2 and NYUv2 datasets.
  arXiv  Detail & Related papers  (2022-05-26T17:00:23Z)
• Learning point embedding for 3D data processing [2.12121796606941]
  Current point-based methods are essentially spatial relationship processing networks. Our architecture, PE-Net, learns the representation of point clouds in high-dimensional space. Experiments show that PE-Net achieves the state-of-the-art performance in multiple challenging datasets.
  arXiv  Detail & Related papers  (2021-07-19T00:25:28Z)
• Refinement of Predicted Missing Parts Enhance Point Cloud Completion [62.997667081978825]
  Point cloud completion is the task of predicting complete geometry from partial observations using a point set representation for a 3D shape. Previous approaches propose neural networks to directly estimate the whole point cloud through encoder-decoder models fed by the incomplete point set. This paper proposes an end-to-end neural network architecture that focuses on computing the missing geometry and merging the known input and the predicted point cloud.
  arXiv  Detail & Related papers  (2020-10-08T22:01:23Z)

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.