VRSO: Visual-Centric Reconstruction for Static Object Annotation
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arXiv
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| Format: | Preprint |
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2024
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| author | Yu, Chenyao Cai, Yingfeng Zhang, Jiaxin Kong, Hui Sui, Wei Yang, Cong |
| author_facet | Yu, Chenyao Cai, Yingfeng Zhang, Jiaxin Kong, Hui Sui, Wei Yang, Cong |
| contents | As a part of the perception results of intelligent driving systems, static object detection (SOD) in 3D space provides crucial cues for driving environment understanding. With the rapid deployment of deep neural networks for SOD tasks, the demand for high-quality training samples soars. The traditional, also reliable, way is manual labelling over the dense LiDAR point clouds and reference images. Though most public driving datasets adopt this strategy to provide SOD ground truth (GT), it is still expensive and time-consuming in practice. This paper introduces VRSO, a visual-centric approach for static object annotation. Experiments on the Waymo Open Dataset show that the mean reprojection error from VRSO annotation is only 2.6 pixels, around four times lower than the Waymo Open Dataset labels (10.6 pixels). VRSO is distinguished in low cost, high efficiency, and high quality: (1) It recovers static objects in 3D space with only camera images as input, and (2) manual annotation is barely involved since GT for SOD tasks is generated based on an automatic reconstruction and annotation pipeline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15026 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VRSO: Visual-Centric Reconstruction for Static Object Annotation Yu, Chenyao Cai, Yingfeng Zhang, Jiaxin Kong, Hui Sui, Wei Yang, Cong Computer Vision and Pattern Recognition As a part of the perception results of intelligent driving systems, static object detection (SOD) in 3D space provides crucial cues for driving environment understanding. With the rapid deployment of deep neural networks for SOD tasks, the demand for high-quality training samples soars. The traditional, also reliable, way is manual labelling over the dense LiDAR point clouds and reference images. Though most public driving datasets adopt this strategy to provide SOD ground truth (GT), it is still expensive and time-consuming in practice. This paper introduces VRSO, a visual-centric approach for static object annotation. Experiments on the Waymo Open Dataset show that the mean reprojection error from VRSO annotation is only 2.6 pixels, around four times lower than the Waymo Open Dataset labels (10.6 pixels). VRSO is distinguished in low cost, high efficiency, and high quality: (1) It recovers static objects in 3D space with only camera images as input, and (2) manual annotation is barely involved since GT for SOD tasks is generated based on an automatic reconstruction and annotation pipeline. |
| title | VRSO: Visual-Centric Reconstruction for Static Object Annotation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.15026 |