Point Cloud Compression: Impact on Object Detection in Outdoor Contexts
Increasing demand for more reliable and safe autonomous driving means that data involved in the various aspects of perception, such as object detection, will become more granular as the number and resolution of sensors progress. Using these data for on-the-fly object detection causes problems relate...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/15/5767 |
| _version_ | 1797440627707740160 |
|---|---|
| author | Luís Garrote João Perdiz Luís A. da Silva Cruz Urbano J. Nunes |
| author_facet | Luís Garrote João Perdiz Luís A. da Silva Cruz Urbano J. Nunes |
| author_sort | Luís Garrote |
| collection | DOAJ |
| description | Increasing demand for more reliable and safe autonomous driving means that data involved in the various aspects of perception, such as object detection, will become more granular as the number and resolution of sensors progress. Using these data for on-the-fly object detection causes problems related to the computational complexity of onboard processing in autonomous vehicles, leading to a desire to offload computation to roadside infrastructure using vehicle-to-infrastructure communication links. The need to transmit sensor data also arises in the context of vehicle fleets exchanging sensor data, over vehicle-to-vehicle communication links. Some types of sensor data modalities, such as Light Detection and Ranging (LiDAR) point clouds, are so voluminous that their transmission is impractical without data compression. With most emerging autonomous driving implementations being anchored on point cloud data, we propose to evaluate the impact of point cloud compression on object detection. To that end, two different object detection architectures are evaluated using point clouds from the KITTI object dataset: raw point clouds and point clouds compressed with a state-of-the-art encoder and three different compression levels. The analysis is extended to the impact of compression on depth maps generated from images projected from the point clouds, with two conversion methods tested. Results show that low-to-medium levels of compression do not have a major impact on object detection performance, especially for larger objects. Results also show that the impact of point cloud compression is lower when detecting objects using depth maps, placing this particular method of point cloud data representation on a competitive footing compared to raw point cloud data. |
| first_indexed | 2024-03-09T12:12:02Z |
| format | Article |
| id | doaj.art-ab067eae404b4088b2f65b4844284e80 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:12:02Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ab067eae404b4088b2f65b4844284e802023-11-30T22:51:44ZengMDPI AGSensors1424-82202022-08-012215576710.3390/s22155767Point Cloud Compression: Impact on Object Detection in Outdoor ContextsLuís Garrote0João Perdiz1Luís A. da Silva Cruz2Urbano J. Nunes3Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, PortugalDepartment of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, PortugalDepartment of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, PortugalDepartment of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, PortugalIncreasing demand for more reliable and safe autonomous driving means that data involved in the various aspects of perception, such as object detection, will become more granular as the number and resolution of sensors progress. Using these data for on-the-fly object detection causes problems related to the computational complexity of onboard processing in autonomous vehicles, leading to a desire to offload computation to roadside infrastructure using vehicle-to-infrastructure communication links. The need to transmit sensor data also arises in the context of vehicle fleets exchanging sensor data, over vehicle-to-vehicle communication links. Some types of sensor data modalities, such as Light Detection and Ranging (LiDAR) point clouds, are so voluminous that their transmission is impractical without data compression. With most emerging autonomous driving implementations being anchored on point cloud data, we propose to evaluate the impact of point cloud compression on object detection. To that end, two different object detection architectures are evaluated using point clouds from the KITTI object dataset: raw point clouds and point clouds compressed with a state-of-the-art encoder and three different compression levels. The analysis is extended to the impact of compression on depth maps generated from images projected from the point clouds, with two conversion methods tested. Results show that low-to-medium levels of compression do not have a major impact on object detection performance, especially for larger objects. Results also show that the impact of point cloud compression is lower when detecting objects using depth maps, placing this particular method of point cloud data representation on a competitive footing compared to raw point cloud data.https://www.mdpi.com/1424-8220/22/15/5767point cloudlossy compressionobject detectiondepth mapsdepth filteringmachine learning |
| spellingShingle | Luís Garrote João Perdiz Luís A. da Silva Cruz Urbano J. Nunes Point Cloud Compression: Impact on Object Detection in Outdoor Contexts Sensors point cloud lossy compression object detection depth maps depth filtering machine learning |
| title | Point Cloud Compression: Impact on Object Detection in Outdoor Contexts |
| title_full | Point Cloud Compression: Impact on Object Detection in Outdoor Contexts |
| title_fullStr | Point Cloud Compression: Impact on Object Detection in Outdoor Contexts |
| title_full_unstemmed | Point Cloud Compression: Impact on Object Detection in Outdoor Contexts |
| title_short | Point Cloud Compression: Impact on Object Detection in Outdoor Contexts |
| title_sort | point cloud compression impact on object detection in outdoor contexts |
| topic | point cloud lossy compression object detection depth maps depth filtering machine learning |
| url | https://www.mdpi.com/1424-8220/22/15/5767 |
| work_keys_str_mv | AT luisgarrote pointcloudcompressionimpactonobjectdetectioninoutdoorcontexts AT joaoperdiz pointcloudcompressionimpactonobjectdetectioninoutdoorcontexts AT luisadasilvacruz pointcloudcompressionimpactonobjectdetectioninoutdoorcontexts AT urbanojnunes pointcloudcompressionimpactonobjectdetectioninoutdoorcontexts |