Point Set Self-Embedding

02/28/2022
by   Ruihui Li, et al.
19

This work presents an innovative method for point set self-embedding, that encodes the structural information of a dense point set into its sparser version in a visual but imperceptible form. The self-embedded point set can function as the ordinary downsampled one and be visualized efficiently on mobile devices. Particularly, we can leverage the self-embedded information to fully restore the original point set for detailed analysis on remote servers. This task is challenging since both the self-embedded point set and the restored point set should resemble the original one. To achieve a learnable self-embedding scheme, we design a novel framework with two jointly-trained networks: one to encode the input point set into its self-embedded sparse point set and the other to leverage the embedded information for inverting the original point set back. Further, we develop a pair of up-shuffle and down-shuffle units in the two networks, and formulate loss terms to encourage the shape similarity and point distribution in the results. Extensive qualitative and quantitative results demonstrate the effectiveness of our method on both synthetic and real-scanned datasets.

READ FULL TEXT

page 7

page 8

page 9

page 16

page 17

page 18

page 20

page 21

research
08/10/2021

Tutorial on the Robust Interior Point Method

We give a short, self-contained proof of the interior point method and i...
research
12/08/2020

SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization

The task of point cloud upsampling aims to acquire dense and uniform poi...
research
06/09/2021

Point Cloud Upsampling via Disentangled Refinement

Point clouds produced by 3D scanning are often sparse, non-uniform, and ...
research
03/03/2020

Unsupervised Learning of Intrinsic Structural Representation Points

Learning structures of 3D shapes is a fundamental problem in the field o...
research
10/28/2022

Fashion-Specific Attributes Interpretation via Dual Gaussian Visual-Semantic Embedding

Several techniques to map various types of components, such as words, at...
research
06/12/2019

Semi-flat minima and saddle points by embedding neural networks to overparameterization

We theoretically study the landscape of the training error for neural ne...
research
03/21/2022

Unsupervised Heterophilous Network Embedding via r-Ego Network Discrimination

Recently, supervised network embedding (NE) has emerged as a predominant...

Please sign up or login with your details

Forgot password? Click here to reset