Saved in:
Bibliographic Details
Main Author: Zhang, Tongxu
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2407.04476
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866917120362676224
author Zhang, Tongxu
author_facet Zhang, Tongxu
contents Point cloud upsampling is crucial for tasks like 3D reconstruction. While existing methods rely on patch-based inputs, and there is no research discussing the differences and principles between point cloud model full input and patch based input. Ergo, we propose a novel approach using whole model inputs i.e. Average Segment input. Our experiments on PU1K and ABC datasets reveal that patch-based inputs consistently outperform whole model inputs. To understand this, we will delve into factors in feature extraction, and network architecture that influence upsampling results.
format Preprint
id arxiv_https___arxiv_org_abs_2407_04476
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Rethinking Data Input for Point Cloud Upsampling
Zhang, Tongxu
Computer Vision and Pattern Recognition
Machine Learning
Point cloud upsampling is crucial for tasks like 3D reconstruction. While existing methods rely on patch-based inputs, and there is no research discussing the differences and principles between point cloud model full input and patch based input. Ergo, we propose a novel approach using whole model inputs i.e. Average Segment input. Our experiments on PU1K and ABC datasets reveal that patch-based inputs consistently outperform whole model inputs. To understand this, we will delve into factors in feature extraction, and network architecture that influence upsampling results.
title Rethinking Data Input for Point Cloud Upsampling
topic Computer Vision and Pattern Recognition
Machine Learning
url https://arxiv.org/abs/2407.04476