Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.15550 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909435870314496 |
|---|---|
| author | Huo, Yixiong Jiang, Guangfeng Wei, Hongyang Liu, Ji Zhang, Song Liu, Han Huang, Xingliang Lu, Mingjie Peng, Jinzhang Li, Dong Tian, Lu Barsoum, Emad |
| author_facet | Huo, Yixiong Jiang, Guangfeng Wei, Hongyang Liu, Ji Zhang, Song Liu, Han Huang, Xingliang Lu, Mingjie Peng, Jinzhang Li, Dong Tian, Lu Barsoum, Emad |
| contents | 3D Gaussian Splatting (3D GS) has gained popularity due to its faster rendering speed and high-quality novel view synthesis. Some researchers have explored using 3D GS for reconstructing driving scenes. However, these methods often rely on various data types, such as depth maps, 3D boxes, and trajectories of moving objects. Additionally, the lack of annotations for synthesized images limits their direct application in downstream tasks. To address these issues, we propose EGSRAL, a 3D GS-based method that relies solely on training images without extra annotations. EGSRAL enhances 3D GS's capability to model both dynamic objects and static backgrounds and introduces a novel adaptor for auto labeling, generating corresponding annotations based on existing annotations. We also propose a grouping strategy for vanilla 3D GS to address perspective issues in rendering large-scale, complex scenes. Our method achieves state-of-the-art performance on multiple datasets without any extra annotation. For example, the PSNR metric reaches 29.04 on the nuScenes dataset. Moreover, our automated labeling can significantly improve the performance of 2D/3D detection tasks. Code is available at https://github.com/jiangxb98/EGSRAL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15550 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EGSRAL: An Enhanced 3D Gaussian Splatting based Renderer with Automated Labeling for Large-Scale Driving Scene Huo, Yixiong Jiang, Guangfeng Wei, Hongyang Liu, Ji Zhang, Song Liu, Han Huang, Xingliang Lu, Mingjie Peng, Jinzhang Li, Dong Tian, Lu Barsoum, Emad Computer Vision and Pattern Recognition 3D Gaussian Splatting (3D GS) has gained popularity due to its faster rendering speed and high-quality novel view synthesis. Some researchers have explored using 3D GS for reconstructing driving scenes. However, these methods often rely on various data types, such as depth maps, 3D boxes, and trajectories of moving objects. Additionally, the lack of annotations for synthesized images limits their direct application in downstream tasks. To address these issues, we propose EGSRAL, a 3D GS-based method that relies solely on training images without extra annotations. EGSRAL enhances 3D GS's capability to model both dynamic objects and static backgrounds and introduces a novel adaptor for auto labeling, generating corresponding annotations based on existing annotations. We also propose a grouping strategy for vanilla 3D GS to address perspective issues in rendering large-scale, complex scenes. Our method achieves state-of-the-art performance on multiple datasets without any extra annotation. For example, the PSNR metric reaches 29.04 on the nuScenes dataset. Moreover, our automated labeling can significantly improve the performance of 2D/3D detection tasks. Code is available at https://github.com/jiangxb98/EGSRAL. |
| title | EGSRAL: An Enhanced 3D Gaussian Splatting based Renderer with Automated Labeling for Large-Scale Driving Scene |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.15550 |