Segmenting 3D Hybrid Scenes via Zero-Shot Learning

• URL: http://arxiv.org/abs/2107.00430v2
• Date: Sun, 4 Jul 2021 09:07:35 GMT
• Title: Segmenting 3D Hybrid Scenes via Zero-Shot Learning
• Authors: Bo Liu, Shuang Deng, Qiulei Dong, Zhanyi Hu
• Abstract summary: This work is to tackle the problem of point cloud semantic segmentation for 3D hybrid scenes under the framework of zero-shot learning. We propose a network to synthesize point features for various classes of objects by leveraging the semantic features of both seen and unseen object classes, called PFNet. The proposed PFNet employs a GAN architecture to synthesize point features, where the semantic relationship between seen-class and unseen-class features is consolidated by adapting a new semantic regularizer. We introduce two benchmarks for algorithmic evaluation by re-organizing the public S3DIS and ScanNet datasets under six different data splits.
• Score: 13.161136148641813
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This work is to tackle the problem of point cloud semantic segmentation for 3D hybrid scenes under the framework of zero-shot learning. Here by hybrid, we mean the scene consists of both seen-class and unseen-class 3D objects, a more general and realistic setting in application. To our knowledge, this problem has not been explored in the literature. To this end, we propose a network to synthesize point features for various classes of objects by leveraging the semantic features of both seen and unseen object classes, called PFNet. The proposed PFNet employs a GAN architecture to synthesize point features, where the semantic relationship between seen-class and unseen-class features is consolidated by adapting a new semantic regularizer, and the synthesized features are used to train a classifier for predicting the labels of the testing 3D scene points. Besides we also introduce two benchmarks for algorithmic evaluation by re-organizing the public S3DIS and ScanNet datasets under six different data splits. Experimental results on the two benchmarks validate our proposed method, and we hope our introduced two benchmarks and methodology could be of help for more research on this new direction.

Related papers

• Segment Any 3D Object with Language [58.471327490684295]
  We introduce Segment any 3D Object with LanguagE (SOLE), a semantic geometric and-aware visual-language learning framework with strong generalizability. Specifically, we propose a multimodal fusion network to incorporate multimodal semantics in both backbone and decoder. Our SOLE outperforms previous methods by a large margin on ScanNetv2, ScanNet200, and Replica benchmarks.
  arXiv  Detail & Related papers  (2024-04-02T17:59:10Z)
• SAI3D: Segment Any Instance in 3D Scenes [68.57002591841034]
  We introduce SAI3D, a novel zero-shot 3D instance segmentation approach. Our method partitions a 3D scene into geometric primitives, which are then progressively merged into 3D instance segmentations. Empirical evaluations on ScanNet, Matterport3D and the more challenging ScanNet++ datasets demonstrate the superiority of our approach.
  arXiv  Detail & Related papers  (2023-12-17T09:05:47Z)
• Dynamic Clustering Transformer Network for Point Cloud Segmentation [23.149220817575195]
  We propose a novel 3D point cloud representation network, called Dynamic Clustering Transformer Network (DCTNet) It has an encoder-decoder architecture, allowing for both local and global feature learning. Our method was evaluated on an object-based dataset (ShapeNet), an urban navigation dataset (Toronto-3D), and a multispectral LiDAR dataset.
  arXiv  Detail & Related papers  (2023-05-30T01:11:05Z)
• CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds [55.44204039410225]
  We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D. Our proposed method first generates some high-quality 3D proposals by leveraging the class-aware local group strategy on the object surface voxels. To recover the features of missed voxels due to incorrect voxel-wise segmentation, we build a fully sparse convolutional RoI pooling module.
  arXiv  Detail & Related papers  (2022-10-09T13:38:48Z)
• 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)
• Background-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregation [12.093182949686781]
  We propose a novel 3D point cloud learning network, referred to as Dynamic Point Feature Aggregation Network (DPFA-Net) DPFA-Net has two variants for semantic segmentation and classification of 3D point clouds. It achieves the state-of-the-art overall accuracy score for semantic segmentation on the S3DIS dataset.
  arXiv  Detail & Related papers  (2021-11-14T05:46:05Z)
• GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network [1.9949920338542213]
  GP-S3Net is a proposal-free approach in which no object proposals are needed to identify the objects. Our new design consists of a novel instance-level network to process the semantic results. Extensive experiments demonstrate that GP-S3Net outperforms the current state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-08-18T21:49:58Z)
• Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes [17.217954254022573]
  We introduce a meta-learning-based method for few-shot 3D shape segmentation where only a few labeled samples are provided for the unseen classes. We demonstrate the superior performance of our proposed on the ShapeNet part dataset under the few-shot scenario, compared with well-established baseline and state-of-the-art semi-supervised methods.
  arXiv  Detail & Related papers  (2021-07-07T01:47:00Z)
• Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection [102.62963605429508]
  Point cloud semantic segmentation plays an essential role in autonomous driving. Current 3D semantic segmentation networks focus on convolutional architectures that perform great for well represented classes. We propose a novel Aware 3D Semantic Detection (DASS) framework that explicitly leverages localization features from an auxiliary 3D object detection task.
  arXiv  Detail & Related papers  (2020-09-22T14:17:40Z)
• Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions [70.0171362989609]
  We propose a novel fine-grained 3D shape classification method named FG3D-Net to capture the fine-grained local details of 3D shapes from multiple rendered views. Our results under the fine-grained 3D shape dataset show that our method outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-05-26T06:53:19Z)

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.