Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation

• URL: http://arxiv.org/abs/2009.01280v1
• Date: Wed, 2 Sep 2020 18:25:25 GMT
• Title: Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation
• Authors: Min Zhang, Pranav Kadam, Shan Liu, C. -C. Jay Kuo
• Abstract summary: Unsupervised feedforward feature learning is proposed for joint classification and segmentation of 3D point clouds. The UFF method exploits statistical correlations of points in a point cloud set to learn shape and point features in a one-pass feedforward manner. It learns global shape features through the encoder and local point features through the encoder-decoder architecture.
• Score: 57.62713515497585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In contrast to supervised backpropagation-based feature learning in deep neural networks (DNNs), an unsupervised feedforward feature (UFF) learning scheme for joint classification and segmentation of 3D point clouds is proposed in this work. The UFF method exploits statistical correlations of points in a point cloud set to learn shape and point features in a one-pass feedforward manner through a cascaded encoder-decoder architecture. It learns global shape features through the encoder and local point features through the concatenated encoder-decoder architecture. The extracted features of an input point cloud are fed to classifiers for shape classification and part segmentation. Experiments are conducted to evaluate the performance of the UFF method. For shape classification, the UFF is superior to existing unsupervised methods and on par with state-of-the-art DNNs. For part segmentation, the UFF outperforms semi-supervised methods and performs slightly worse than DNNs.

Related papers

• PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud Videos [58.18707835387484]
  We propose a contrastive mask prediction framework for self-supervised learning on point cloud videos. PointCMP employs a two-branch structure to achieve simultaneous learning of both local and globaltemporal information. Our framework achieves the state-of-the-art performance on benchmark datasets and outperforms existing full-supervised counterparts.
  arXiv  Detail & Related papers  (2023-05-06T15:47:48Z)
• Learning Latent Part-Whole Hierarchies for Point Clouds [41.288934432615676]
  We propose an encoder-decoder style latent variable model that explicitly learns the part-whole hierarchies for the point cloud segmentation. The proposed method achieves state-of-the-art performance in not only top-level part segmentation but also middle-level latent subpart segmentation.
  arXiv  Detail & Related papers  (2022-11-14T03:17:33Z)
• Upsampling Autoencoder for Self-Supervised Point Cloud Learning [11.19408173558718]
  We propose a self-supervised pretraining model for point cloud learning without human annotations. Upsampling operation encourages the network to capture both high-level semantic information and low-level geometric information of the point cloud. We find that our UAE outperforms previous state-of-the-art methods in shape classification, part segmentation and point cloud upsampling tasks.
  arXiv  Detail & Related papers  (2022-03-21T07:20:37Z)
• Unsupervised Representation Learning for 3D Point Cloud Data [66.92077180228634]
  We propose a simple yet effective approach for unsupervised point cloud learning. In particular, we identify a very useful transformation which generates a good contrastive version of an original point cloud. We conduct experiments on three downstream tasks which are 3D object classification, shape part segmentation and scene segmentation.
  arXiv  Detail & Related papers  (2021-10-13T10:52:45Z)
• 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 Discriminative Learning for Unsupervised Representation Learning on 3D Point Clouds [54.31515001741987]
  We propose a point discriminative learning method for unsupervised representation learning on 3D point clouds. We achieve this by imposing a novel point discrimination loss on the middle level and global level point features. Our method learns powerful representations and achieves new state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-08-04T15:11:48Z)
• Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning [41.83979510282989]
  We bring the first omni-scale supervision method to point cloud segmentation via the proposed gradual Receptive Field Component Reasoning (RFCR) Our method brings new state-of-the-art performances for S3DIS as well as Semantic3D and ranks the 1st in the ScanNet benchmark among all the point-based methods.
  arXiv  Detail & Related papers  (2021-05-21T08:32:02Z)
• iffDetector: Inference-aware Feature Filtering for Object Detection [70.8678270164057]
  We introduce a generic Inference-aware Feature Filtering (IFF) module that can easily be combined with modern detectors. IFF performs closed-loop optimization by leveraging high-level semantics to enhance the convolutional features. IFF can be fused with CNN-based object detectors in a plug-and-play manner with negligible computational cost overhead.
  arXiv  Detail & Related papers  (2020-06-23T02:57:29Z)

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.