Aggregated Time Series Features in a Voxel-Based Network Architecture
Using point cloud sequences is a popular way to harness the additional information represented in the time domain in order to enhance the performance of 3D object detector neural networks. However, it is not trivial to decide which abstraction level should the additional information presented to the...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10855412/ |
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| author | Zsolt Vincze Andras Rovid |
| author_facet | Zsolt Vincze Andras Rovid |
| author_sort | Zsolt Vincze |
| collection | DOAJ |
| description | Using point cloud sequences is a popular way to harness the additional information represented in the time domain in order to enhance the performance of 3D object detector neural networks. However, it is not trivial to decide which abstraction level should the additional information presented to the network, or what is the point in the architecture, where aggregating the additional information is most beneficial. In this article, the authors propose various voxel-based networks and analyze their performance in relation to the abstraction level of the time series data. During the evaluation, the authors examine the object detection performance of a popular voxel-based neural network with its original architecture and several variants where the time domain related features were propagated through the network and aggregated at different stages of processing. Based on the evaluation results, a conclusion is drawn regarding the abstraction level at which the time-series aggregation step is performed in order to improve the performance of the baseline voxel-based detector. |
| format | Article |
| id | doaj-art-041371634e8444ff9ff518b992fdd723 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-041371634e8444ff9ff518b992fdd7232025-01-31T23:04:44ZengIEEEIEEE Access2169-35362025-01-0113193291933910.1109/ACCESS.2025.353515110855412Aggregated Time Series Features in a Voxel-Based Network ArchitectureZsolt Vincze0https://orcid.org/0000-0002-5813-3530Andras Rovid1https://orcid.org/0000-0002-9044-1760Department of Automotive Technologies, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Budapest, HungaryDepartment of Automotive Technologies, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Budapest, HungaryUsing point cloud sequences is a popular way to harness the additional information represented in the time domain in order to enhance the performance of 3D object detector neural networks. However, it is not trivial to decide which abstraction level should the additional information presented to the network, or what is the point in the architecture, where aggregating the additional information is most beneficial. In this article, the authors propose various voxel-based networks and analyze their performance in relation to the abstraction level of the time series data. During the evaluation, the authors examine the object detection performance of a popular voxel-based neural network with its original architecture and several variants where the time domain related features were propagated through the network and aggregated at different stages of processing. Based on the evaluation results, a conclusion is drawn regarding the abstraction level at which the time-series aggregation step is performed in order to improve the performance of the baseline voxel-based detector.https://ieeexplore.ieee.org/document/10855412/Neural networksLiDAR point cloudpoint cloud sequencetime seriesobject detection |
| spellingShingle | Zsolt Vincze Andras Rovid Aggregated Time Series Features in a Voxel-Based Network Architecture IEEE Access Neural networks LiDAR point cloud point cloud sequence time series object detection |
| title | Aggregated Time Series Features in a Voxel-Based Network Architecture |
| title_full | Aggregated Time Series Features in a Voxel-Based Network Architecture |
| title_fullStr | Aggregated Time Series Features in a Voxel-Based Network Architecture |
| title_full_unstemmed | Aggregated Time Series Features in a Voxel-Based Network Architecture |
| title_short | Aggregated Time Series Features in a Voxel-Based Network Architecture |
| title_sort | aggregated time series features in a voxel based network architecture |
| topic | Neural networks LiDAR point cloud point cloud sequence time series object detection |
| url | https://ieeexplore.ieee.org/document/10855412/ |
| work_keys_str_mv | AT zsoltvincze aggregatedtimeseriesfeaturesinavoxelbasednetworkarchitecture AT andrasrovid aggregatedtimeseriesfeaturesinavoxelbasednetworkarchitecture |