Rotation Transformation Network: Learning View-Invariant Point Cloud for Classification and Segmentation

07/07/2021
by   Shuang Deng, et al.
0

Many recent works show that a spatial manipulation module could boost the performances of deep neural networks (DNNs) for 3D point cloud analysis. In this paper, we aim to provide an insight into spatial manipulation modules. Firstly, we find that the smaller the rotational degree of freedom (RDF) of objects is, the more easily these objects are handled by these DNNs. Then, we investigate the effect of the popular T-Net module and find that it could not reduce the RDF of objects. Motivated by the above two issues, we propose a rotation transformation network for point cloud analysis, called RTN, which could reduce the RDF of input 3D objects to 0. The RTN could be seamlessly inserted into many existing DNNs for point cloud analysis. Extensive experimental results on 3D point cloud classification and segmentation tasks demonstrate that the proposed RTN could improve the performances of several state-of-the-art methods significantly.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/05/2021

Interpreting Representation Quality of DNNs for 3D Point Cloud Processing

In this paper, we evaluate the quality of knowledge representations enco...
research
03/14/2023

Frequency-Modulated Point Cloud Rendering with Easy Editing

We develop an effective point cloud rendering pipeline for novel view sy...
research
03/01/2023

Nearest Neighbors Meet Deep Neural Networks for Point Cloud Analysis

Performances on standard 3D point cloud benchmarks have plateaued, resul...
research
11/20/2019

Utility Analysis of Network Architectures for 3D Point Cloud Processing

In this paper, we diagnose deep neural networks for 3D point cloud proce...
research
03/28/2021

Noise Injection-based Regularization for Point Cloud Processing

Noise injection-based regularization, such as Dropout, has been widely u...
research
09/15/2020

A Self Contour-based Rotation and Translation-Invariant Transformation for Point Clouds Recognition

Recently, several direct processing point cloud models have achieved sta...
research
10/29/2019

POIRot: A rotation invariant omni-directional pointnet

Point-cloud is an efficient way to represent 3D world. Analysis of point...

Please sign up or login with your details

Forgot password? Click here to reset