EVALUATING THE POTENTIAL OF CLUSTERING TECHNIQUES FOR 3D OBJECT EXTRACTION FROM LIDAR DATA

2006 ◽  
Keyword(s):  
Object Extraction ◽  
Lidar Data ◽  
Clustering Techniques ◽  
3D Object

Deep 3D Object Detection Networks Using LiDAR Data: A Review

2021 ◽  
Vol 21 (2) ◽  
pp. 1152-1171
Author(s):  
Yutian Wu ◽  
Yueyu Wang ◽  
Shuwei Zhang ◽  
Harutoshi Ogai
Keyword(s):  
Object Detection ◽  
Lidar Data ◽  
3D Object ◽  
3D Object Detection

scholarly journals Optimization of the PointPillars network for 3D object detection in point clouds

2020 ◽  
Author(s):  
Joanna Stanisz ◽  
Konrad Lis ◽  
Tomasz Kryjak ◽  
Marek Gorgon
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Main Part ◽  
Point Clouds ◽  
Lidar Data ◽  
Detection Accuracy ◽  
3D Object ◽  
Fold Reduction ◽  
Low Energy Consumption ◽  
3D Object Detection

In this paper we present our research on the optimisation of a deep neural network for 3D object detection in a point cloud. Techniques like quantisation and pruning available in the Brevitas and PyTorch tools were used. We performed the experiments for the PointPillars network, which offers a reasonable compromise between detection accuracy and calculation complexity. The aim of this work was to propose a variant of the network which we will ultimately implement in an FPGA device. This will allow for real-time LiDAR data processing with low energy consumption. The obtained results indicate that even a significant quantisation from 32-bit floating point to 2-bit integer in the main part of the algorithm, results in 5%-9% decrease of the detection accuracy, while allowing for almost a 16-fold reduction in size of the model.

scholarly journals A Novel Regional Fusion Network for 3D Object Detection based on RGB Images and Point Clouds

2021 ◽  
Author(s):  
Hung-Hao Chen ◽  
Chia-Hung Wang ◽  
Hsueh-Wei Chen ◽  
Pei-Yung Hsiao ◽  
Li-Chen Fu ◽  
...  
Keyword(s):  
Object Detection ◽  
Receptive Fields ◽  
Point Clouds ◽  
Detection Methods ◽  
Lidar Data ◽  
3D Object ◽  
Multi Scale ◽  
Interest Level ◽  
Rgb Images ◽  
3D Object Detection

The current fusion-based methods transform LiDAR data into bird’s eye view (BEV) representations or 3D voxel, leading to information loss and heavy computation cost of 3D convolution. In contrast, we directly consume raw point clouds and perform fusion between two modalities. We employ the concept of region proposal network to generate proposals from two streams, respectively. In order to make two sensors compensate the weakness of each other, we utilize the calibration parameters to project proposals from one stream onto the other. With the proposed multi-scale feature aggregation module, we are able to combine the extracted regionof-interest-level (RoI-level) features of RGB stream from different receptive fields, resulting in fertilizing feature richness. Experiments on KITTI dataset show that our proposed network outperforms other fusion-based methods with meaningful improvements as compared to 3D object detection methods under challenging setting.

Multi-3D-Object Tracking by Fusing RGB and 3D-LiDAR Data

2019 ◽  
Author(s):  
He Wang ◽  
Jianwu Fang ◽  
Saijia Cui ◽  
Hongke Xu ◽  
Jianru Xue
Keyword(s):  
Object Tracking ◽  
Lidar Data ◽  
3D Object ◽  
3D Lidar
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