Angle Based Feature Learning in GNN for 3D Object Detection using Point Cloud

08/02/2021
by   Md Afzal Ansari, et al.
0

In this paper, we present new feature encoding methods for Detection of 3D objects in point clouds. We used a graph neural network (GNN) for Detection of 3D objects namely cars, pedestrians, and cyclists. Feature encoding is one of the important steps in Detection of 3D objects. The dataset used is point cloud data which is irregular and unstructured and it needs to be encoded in such a way that ensures better feature encapsulation. Earlier works have used relative distance as one of the methods to encode the features. These methods are not resistant to rotation variance problems in Graph Neural Networks. We have included angular-based measures while performing feature encoding in graph neural networks. Along with that, we have performed a comparison between other methods like Absolute, Relative, Euclidean distances, and a combination of the Angle and Relative methods. The model is trained and evaluated on the subset of the KITTI object detection benchmark dataset under resource constraints. Our results demonstrate that a combination of angle measures and relative distance has performed better than other methods. In comparison to the baseline method(relative), it achieved better performance. We also performed time analysis of various feature encoding methods.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

03/02/2020

Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud

In this paper, we propose a graph neural network to detect objects from ...
09/17/2020

Dynamic Edge Weights in Graph Neural Networks for 3D Object Detection

A robust and accurate 3D detection system is an integral part of autonom...
12/18/2020

PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection

LiDAR-based 3D object detection is an important task for autonomous driv...
11/17/2017

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

Accurate detection of objects in 3D point clouds is a central problem in...
01/31/2021

PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection

3D object detection is receiving increasing attention from both industry...
05/28/2021

Symmetry-driven graph neural networks

Exploiting symmetries and invariance in data is a powerful, yet not full...
06/10/2021

Spatially Invariant Unsupervised 3D Object Segmentation with Graph Neural Networks

In this paper, we tackle the problem of unsupervised 3D object segmentat...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.