Related papers: Mapping in a cycle: Sinkhorn regularized unsupervised learning for point cloud shapes

Mapping in a cycle: Sinkhorn regularized unsupervised learning for point cloud shapes

URL: http://arxiv.org/abs/2007.09594v1
Date: Sun, 19 Jul 2020 05:21:33 GMT
Title: Mapping in a cycle: Sinkhorn regularized unsupervised learning for point cloud shapes
Authors: Lei Yang, Wenxi Liu, Zhiming Cui, Nenglun Chen, Wenping Wang
Abstract summary: We propose an unsupervised learning framework for finding dense correspondences between point cloud shapes. In order to learn discriminative pointwise features from point cloud data, we incorporate in the formulation a regularization term based on Sinkhorn normalization. We show that the learned pointwise features can be leveraged by supervised methods to improve the part segmentation performance.
Score: 47.49826669394906
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We propose an unsupervised learning framework with the pretext task of finding dense correspondences between point cloud shapes from the same category based on the cycle-consistency formulation. In order to learn discriminative pointwise features from point cloud data, we incorporate in the formulation a regularization term based on Sinkhorn normalization to enhance the learned pointwise mappings to be as bijective as possible. Besides, a random rigid transform of the source shape is introduced to form a triplet cycle to improve the model's robustness against perturbations. Comprehensive experiments demonstrate that the learned pointwise features through our framework benefits various point cloud analysis tasks, e.g. partial shape registration and keypoint transfer. We also show that the learned pointwise features can be leveraged by supervised methods to improve the part segmentation performance with either the full training dataset or just a small portion of it.

Related papers

Curvature Informed Furthest Point Sampling [0.0]
We introduce a reinforcement learning-based sampling algorithm that enhances furthest point sampling (FPS) Our approach ranks points by combining FPS-derived soft ranks with curvature scores computed by a deep neural network. We provide comprehensive ablation studies, with both qualitative and quantitative insights into the effect of each feature on performance.
arXiv Detail & Related papers (2024-11-25T23:58:38Z)
Unsupervised Non-Rigid Point Cloud Matching through Large Vision Models [1.3030624795284795]
We propose a learning-based framework for non-rigid point cloud matching. Key insight is to incorporate semantic features derived from large vision models (LVMs) Our framework effectively leverages the structural information contained in the semantic features to address ambiguities arise from self-similarities among local geometries.
arXiv Detail & Related papers (2024-08-16T07:02:19Z)
Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos [71.20376514273367]
We propose a unified point cloud video self-supervised learning framework for object-centric and scene-centric data. Our method outperforms supervised counterparts on a wide range of downstream tasks.
arXiv Detail & Related papers (2023-08-18T02:17:47Z)
Rethinking Rotation Invariance with Point Cloud Registration [18.829454172955202]
We propose an effective framework for rotation invariance learning via three sequential stages, namely rotation-invariant shape encoding, aligned feature integration, and deep feature registration. Experimental results on 3D shape classification, part segmentation, and retrieval tasks prove the feasibility of our work.
arXiv Detail & Related papers (2022-12-31T08:17:09Z)
EditVAE: Unsupervised Part-Aware Controllable 3D Point Cloud Shape Generation [19.817166425038753]
This paper tackles the problem of parts-aware point cloud generation. A simple modification of the Variational Auto-Encoder yields a joint model of the point cloud itself. In addition to the flexibility afforded by our disentangled representation, the inductive bias introduced by our joint modelling approach yields the state-of-the-art experimental results on the ShapeNet dataset.
arXiv Detail & Related papers (2021-10-13T12:38:01Z)
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)
PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features [91.2054994193218]
We propose a point-set learning framework PRIN, focusing on rotation invariant feature extraction in point clouds analysis. In addition, we extend PRIN to a sparse version called SPRIN, which directly operates on sparse point clouds. Results show that, on the dataset with randomly rotated point clouds, SPRIN demonstrates better performance than state-of-the-art methods without any data augmentation.
arXiv Detail & Related papers (2021-02-24T06:44:09Z)
Shape-Oriented Convolution Neural Network for Point Cloud Analysis [59.405388577930616]
Point cloud is a principal data structure adopted for 3D geometric information encoding. Shape-oriented message passing scheme dubbed ShapeConv is proposed to focus on the representation learning of the underlying shape formed by each local neighboring point.
arXiv Detail & Related papers (2020-04-20T16:11:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.