Elaboration on Occupancy Networks and Resizing Strategy

Occupancy networks introduces the decision boundary of neural network classifier to express arbitrarily complex three-dimensional topological structure, thereby solving the problem of incompatibility between the efficiency of calculation and the validity of storage in the field of 3D reconstruction....

Full description

Saved in:
Bibliographic Details
Main Authors: Shengyu, Song, Gu, Xiaofeng, Ping, Kuang, Xu, Yuanyuan
Format: Conference Proceeding
Language:English
Subjects:
3D reconstruction
Computational modeling
image reconstruction
Isosurfaces
neural nets
neural network classifier
occupancy network
Occupancy Networks
resizing strategy
Single object
single-object 3D reconstruction effect
Solid modeling
Task analysis
Three-dimensional displays
Training
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Occupancy networks introduces the decision boundary of neural network classifier to express arbitrarily complex three-dimensional topological structure, thereby solving the problem of incompatibility between the efficiency of calculation and the validity of storage in the field of 3D reconstruction. We briefly introduced the principle of occupancy network and its single-object 3D reconstruction effect. When further applying occupancy network to real online images, we propose a feasible resizing strategy to improve the versatility of occupancy network. We believe this will help occupancy network move towards a broader scene.
ISSN:2576-8964
DOI:10.1109/ICCWAMTIP51612.2020.9317440